__author__ = "Bill MacCartney (wcmac@cs.stanford.edu)"
__version__ = "CS224U, Stanford, Spring 2018"
This codebook illustrates an approach to relation extraction using distant supervision. It uses a simplified version of the approach taken by Mintz et al. in their 2009 paper, Distant supervision for relation extraction without labeled data. If you haven't yet read that paper, read it now! The rest of the codebook will make a lot more sense after you're familiar with it.
Relation extraction is the task of extracting from natural language text relational triples such as:
(founders, SpaceX, Elon_Musk)
(has_spouse, Elon_Musk, Talulah_Riley)
(worked_at, Elon_Musk, Tesla_Motors)
If we can accumulate a large knowledge base (KB) of relational triples, we can use it to power question answering and other applications. Building a KB manually is slow and expensive, but much of the knowledge we'd like to capture is already expressed in abundant text on the web. The aim of relation extraction, therefore, is to accelerate the construction of new KBs — and facilitate the ongoing curation of existing KBs — by extracting relational triples from natural language text.
An obvious way to start is to write down a few patterns which express each relation. For example, we can use the pattern "X is the founder of Y" to find new instances of the founders
relation. If we search a large corpus, we may find the phrase "Elon Musk is the founder of SpaceX", which we can use as evidence for the relational triple (founders, SpaceX, Elon_Musk)
.
Unfortunately, this approach doesn't get us very far. The central challenge of relation extraction is the fantastic diversity of language, the multitude of possible ways to express a given relation. For example, each of the following sentences expressed the relational triple (founders, SpaceX, Elon_Musk)
:
The patterns which connect "Elon Musk" with "SpaceX" in these examples are not ones we could have easily anticipated. To do relation extraction effectively, we need to go beyond hand-built patterns.
Effective relation extraction will require applying machine learning methods. The natural place to start is with supervised learning. This means training an extraction model from a dataset of examples which have been labeled with the target output. Sentences like the three examples above would be annotated with the founders
relation, but we'd also have sentences which include "Elon Musk" and "SpaceX" but do not express the founders
relation, such as:
Such "negative examples" would be labeled as such, and the fully-supervised model would then be able to learn from both positive and negative examples the linguistic patterns that indicate each relation.
The difficulty with the fully-supervised approach is the cost of generating training data. Because of the great diversity of linguistic expression, our model will need lots and lots of training data: at least tens of thousands of examples, although hundreds of thousands or millions would be much better. But labeling the examples is just a slow and expensive as building the KB by hand would be.
The goal of distant supervision is to capture the benefits of supervised learning without paying the cost of labeling training data. Instead of labeling extraction examples by hand, we use existing relational triples to automatically identify extraction examples in a large corpus. For example, if we already have in our KB the relational triple (founders, SpaceX, Elon_Musk)
, we can search a large corpus for sentences in which "SpaceX" and "Elon Musk" co-occur, make the (unreliable!) assumption that all the sentences express the founder
relation, and then use them as training data for a learned model to identify new instances of the founder
relation — all without doing any manual labeling.
This is a powerful idea, but it has two limitations. The first is that, inevitably, some of the sentences in which "SpaceX" and "Elon Musk" co-occur will not express the founder
relation — like the BFR example above. By making the blind assumption that all such sentences do express the founder
relation, we are essentially injecting noise into our training data, and making it harder for our learning algorithms to learn good models. Distant supervision is effective in spite of this problem because it makes it possible to leverage vastly greater quantities of training data, and the benefit of more data outweighs the harm of noisier data.
The second limitation is that we need an existing KB to start from. We can only train a model to extract new instances of the founders
relation if we already have many instances of the founders
relation. Thus, while distant supervision is a great way to extend an existing KB, it's not useful for creating a KB containing new relations from scratch.
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rel_ext_data_home
below.)rel_ext_data_home = 'rel_ext_data'
import gzip
import numpy as np
import random
import os
from collections import Counter, defaultdict, namedtuple
from sklearn.feature_extraction import DictVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import precision_recall_fscore_support
from sklearn.model_selection import train_test_split
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As usual when we're doing NLP, we need to start with a corpus — a large sample of natural language text. And because our goal is to do relation extraction with distant supervision, we need to be able to identify entities in the text and connect them to a knowledge base of relations between entities. So, we need a corpus in which entity mentions are annotated with entity resolutions which map them to a unique, unambiguous identifiers. Entity resolution serves two purposes:
The corpus we'll use for this project is derived from the Wikilinks dataset announced by Google in 2013. This dataset contains over 40M mentions of 3M distinct entities spanning 10M webpages. It provides entity resolutions by mapping each entity mention to a Wikipedia URL.
Now, in order to do relation extraction, we actually need pairs of entity mentions, and it's important to have the context around and between the two mentions. Fortunately, UMass has provided an expanded version of Wikilinks which includes the context around each entity mention. We've written code to stitch together pairs of entity mentions along with their contexts, and we've filtered the examples extensively. The result is a compact corpus suitable for our purposes. Let's take a closer look.
Example = namedtuple('Example',
'entity_1, entity_2, left, mention_1, middle, mention_2, right, '
'left_POS, mention_1_POS, middle_POS, mention_2_POS, right_POS')
def read_examples():
examples = []
path = os.path.join(rel_ext_data_home, 'corpus.tsv.gz')
print('Reading examples from {}'.format(path))
with gzip.open(path) as f:
for line in f:
fields = line[:-1].decode('utf-8').split('\t')
examples.append(Example(*fields))
print('Read {} examples'.format(len(examples)))
return examples
examples = read_examples()
Reading examples from rel_ext_data/corpus.tsv.gz Read 414123 examples
Great, that's a lot of examples! Let's take a closer look at one.
print(examples[1])
Example(entity_1='New_Mexico', entity_2='Arizona', left='to all Spanish-occupied lands . The horno has a beehive shape and uses wood as the only heat source . The procedure still used in parts of', mention_1='New Mexico', middle='and', mention_2='Arizona', right='is to build a fire inside the Horno and , when the proper amount of time has passed , remove the embers and ashes and insert the', left_POS='to/TO all/DT Spanish-occupied/JJ lands/NNS ./. The/DT horno/NN has/VBZ a/DT beehive/NN shape/NN and/CC uses/VBZ wood/NN as/IN the/DT only/JJ heat/NN source/NN ./. The/DT procedure/NN still/RB used/VBN in/IN parts/NNS of/IN', mention_1_POS='New/NNP Mexico/NNP', middle_POS='and/CC', mention_2_POS='Arizona/NNP', right_POS='is/VBZ to/TO build/VB a/DT fire/NN inside/IN the/DT Horno/NNP and/CC ,/, when/WRB the/DT proper/JJ amount/NN of/IN time/NN has/VBZ passed/VBN ,/, remove/VB the/DT embers/NNS and/CC ashes/NNS and/CC insert/VB the/DT')
Every example represents a fragment of webpage text containing two entity mentions. The first two fields, entity_1
and entity_2
, contain unique identifiers for the two entities mentioned. We name entities using Wiki IDs, which you can think of as the last portion of a Wikipedia URL. Thus the Wiki ID Barack_Obama
designates the entity described by https://en.wikipedia.org/wiki/Barack_Obama.
The next five fields represent the text surrounding the two mentions, divided into five chunks: left
contains the text before the first mention, mention_1
is the first mention itself, middle
contains the text between the two mentions, mention_2
is the second mention, and the field right
contains the text after the second mention. Thus, we can reconstruct the context as a single string like this:
ex = examples[1]
' '.join((ex.left, ex.mention_1, ex.middle, ex.mention_2, ex.right))
'to all Spanish-occupied lands . The horno has a beehive shape and uses wood as the only heat source . The procedure still used in parts of New Mexico and Arizona is to build a fire inside the Horno and , when the proper amount of time has passed , remove the embers and ashes and insert the'
The last five fields contain the same five chunks of text, but this time annotated with part-of-speech (POS) tags, which may turn out to be useful when we start building models for relation extraction.
Let's look at the distribution of entities over the corpus. How many entities are there, and what are the most common ones?
counter = Counter()
for example in examples:
counter[example.entity_1] += 1
counter[example.entity_2] += 1
print('The corpus contains {} entities'.format(len(counter)))
counts = sorted([(count, key) for key, count in counter.items()], reverse=True)
print('The most common entities are:')
for count, key in counts[:20]:
print('{:10d} {}'.format(count, key))
The corpus contains 107820 entities The most common entities are: 9399 India 6214 England 4585 Germany 4486 France 4128 Australia 3939 China 3930 Canada 3897 Italy 3368 California 3125 Pakistan 3103 Europe 3097 New_York_City 3025 London 2470 Japan 2468 United_Kingdom 2279 New_Zealand 2275 New_York 2259 Spain 2132 Philippines 2120 Asia
Because we're frequently going to want to retrieve corpus examples containing specific entities, it will be convenient to create a Corpus
class which holds not only the examples themselves, but also a precomputed index.
class Corpus():
def __init__(self, examples):
self._examples = examples
self._examples_by_entities = {}
self._index_examples_by_entities()
def _index_examples_by_entities(self):
for ex in self._examples:
if ex.entity_1 not in self._examples_by_entities:
self._examples_by_entities[ex.entity_1] = {}
if ex.entity_2 not in self._examples_by_entities[ex.entity_1]:
self._examples_by_entities[ex.entity_1][ex.entity_2] = []
self._examples_by_entities[ex.entity_1][ex.entity_2].append(ex)
def get_examples(self):
return iter(self._examples)
def get_examples_for_entities(self, e1, e2):
try:
return self._examples_by_entities[e1][e2]
except KeyError:
return []
def __repr__(self):
return 'Corpus with {} examples'.format(len(self._examples))
corpus = Corpus(examples)
corpus
Corpus with 414123 examples
The main benefit we gain from the Corpus
class is the ability to retrieve examples containing specific entities. Let's find examples containing Steve_Jobs
and Pixar
.
def show_examples_for_pair(e1, e2, corpus):
exs = corpus.get_examples_for_entities(e1, e2)
if exs:
print('The first of {} examples for {} and {} is:'.format(len(exs), e1, e2))
print(exs[0])
else:
print('No examples for {} and {} is:'.format(e1, e2))
show_examples_for_pair('Steve_Jobs', 'Pixar', corpus)
The first of 9 examples for Steve_Jobs and Pixar is: Example(entity_1='Steve_Jobs', entity_2='Pixar', left='of visual effects on films like The Abyss ( 1989 ) , Terminator 2 ( 1991 ) and Jurassic Park ( 1993 ) The computer graphics division of ILM was bought by', mention_1='Steve Jobs', middle='and became', mention_2='Pixar', right=', who would go on to make several groundbreaking animated films starting with Toy Story ( 1995 ) – more information on the history of that here', left_POS='of/IN visual/JJ effects/NNS on/IN films/NNS like/IN The/DT Abyss/NN -LRB-/-LRB- 1989/CD -RRB-/-RRB- ,/, Terminator/NNP 2/CD -LRB-/-LRB- 1991/CD -RRB-/-RRB- and/CC Jurassic/JJ Park/NN -LRB-/-LRB- 1993/CD -RRB-/-RRB- The/DT computer/NN graphics/NNS division/NN of/IN ILM/NNP was/VBD bought/VBN by/IN', mention_1_POS='Steve/NNP Jobs/NNP', middle_POS='and/CC became/VBD', mention_2_POS='Pixar/NNP', right_POS=',/, who/WP would/MD go/VB on/IN to/TO make/VB several/JJ groundbreaking/VBG animated/JJ films/NNS starting/VBG with/IN Toy/NNP Story/NNP -LRB-/-LRB- 1995/CD -RRB-/-RRB- --/: more/JJR information/NN on/IN the/DT history/NN of/IN that/DT here/RB')
Actually, this might not be all of the examples containing Steve_Jobs
and Pixar
. It's only the examples where Steve_Jobs
was mentioned first and Pixar
second. There may be additional examples that have them in the reverse order. Let's check.
show_examples_for_pair('Pixar', 'Steve_Jobs', corpus)
The first of 2 examples for Pixar and Steve_Jobs is: Example(entity_1='Pixar', entity_2='Steve_Jobs', left='in the visual accompaniment to his recordings of Bach ’ s Six Suites for Unaccompanied Cello . Ma has also been seen with Apple Inc. and former', mention_1='Pixar', middle='CEO', mention_2='Steve Jobs', right='. Ma is often invited to press events for Jobs ’ s companies , and has performed on stage during event keynote presentations , as well as appearing in', left_POS="in/IN the/DT visual/JJ accompaniment/NN to/TO his/PRP$ recordings/NNS of/IN Bach/NNP '/POS s/NNS Six/CD Suites/NNP for/IN Unaccompanied/NNP Cello/NNP ./. Ma/NNP has/VBZ also/RB been/VBN seen/VBN with/IN Apple/NNP Inc./NNP and/CC former/JJ", mention_1_POS='Pixar/NNP', middle_POS='CEO/NNP', mention_2_POS='Steve/NNP Jobs/NNP', right_POS="./. Ma/NNP is/VBZ often/RB invited/VBN to/TO press/VB events/NNS for/IN Jobs/NNP '/POS s/NNS companies/NNS ,/, and/CC has/VBZ performed/VBN on/IN stage/NN during/IN event/NN keynote/NN presentations/NNS ,/, as/RB well/RB as/IN appearing/VBG in/IN")
Sure enough. Going forward, we'll have to remember to check both "directions" when we're looking for examples contains a specific pair of entities.
This corpus is not without flaws. As you get more familiar with it, you will likely discover that it contains many examples that are nearly — but not exactly — duplicates. This seems to be a consequence of the web document sampling methodology that was used in the construction of the Wikilinks dataset. However, despite a few warts, it will serve our purposes.
One thing this corpus does not include is any annotation about relations. Thus, it could not be used for the fully-supervised approach to relation extraction, because the fully-supervised approach requires that each pair of entity mentions be annotated with the relation (if any) that holds between the two entities. In order to make any headway, we'll need to connect the corpus with an external source of knowledge about relations. We need a knowledge base.
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The data distribution for this unit includes a knowledge base (KB) ultimately derived from Freebase. Unfortunately, Freebase was shut down in 2016, but the Freebase data is still available from various sources and in various forms. The KB included here was extracted from the Freebase Easy data dump.
The KB is a collection of relational triples, each consisting of a relation, a subject, and an object. For example, here are three triples from the KB:
(place_of_birth, Barack_Obama, Honolulu)
(has_spouse, Barack_Obama, Michelle_Obama)
(author, The_Audacity_of_Hope, Barack_Obama)
As you might guess:
place_of_birth
or has_spouse
.Let's write some code to read the KB so that we can take a closer look.
KBTriple = namedtuple('KBTriple', 'rel, sbj, obj')
def read_kb_triples():
kb_triples = []
path = os.path.join(rel_ext_data_home, 'kb.tsv.gz')
print('Reading KB triples from {} ...'.format(path))
with gzip.open(path) as f:
for line in f:
rel, sbj, obj = line[:-1].decode('utf-8').split('\t')
kb_triples.append(KBTriple(rel, sbj, obj))
print('Read {} KB triples'.format(len(kb_triples)))
return kb_triples
kb_triples = read_kb_triples()
Reading KB triples from rel_ext_data/kb.tsv.gz ... Read 56575 KB triples
OK great, we have a KB!
Now, just as we did for the corpus, we'll create a KB
class to store the KB triples and some associated indexes. We'll want to be able to look up KB triples both by relation and by entities, so we'll create indexes for both of those access patterns.
class KB():
def __init__(self, kb_triples):
self._kb_triples = kb_triples
self._all_relations = []
self._all_entity_pairs = []
self._kb_triples_by_relation = {}
self._kb_triples_by_entities = {}
self._collect_all_entity_pairs()
self._index_kb_triples_by_relation()
self._index_kb_triples_by_entities()
def _collect_all_entity_pairs(self):
pairs = set()
for kbt in self._kb_triples:
pairs.add((kbt.sbj, kbt.obj))
self._all_entity_pairs = sorted(list(pairs))
def _index_kb_triples_by_relation(self):
for kbt in self._kb_triples:
if kbt.rel not in self._kb_triples_by_relation:
self._kb_triples_by_relation[kbt.rel] = []
self._kb_triples_by_relation[kbt.rel].append(kbt)
self._all_relations = sorted(list(self._kb_triples_by_relation))
def _index_kb_triples_by_entities(self):
for kbt in self._kb_triples:
if kbt.sbj not in self._kb_triples_by_entities:
self._kb_triples_by_entities[kbt.sbj] = {}
if kbt.obj not in self._kb_triples_by_entities[kbt.sbj]:
self._kb_triples_by_entities[kbt.sbj][kbt.obj] = []
self._kb_triples_by_entities[kbt.sbj][kbt.obj].append(kbt)
def get_triples(self):
return iter(self._kb_triples)
def get_all_relations(self):
return self._all_relations
def get_all_entity_pairs(self):
return self._all_entity_pairs
def get_triples_for_relation(self, rel):
try:
return self._kb_triples_by_relation[rel]
except KeyError:
return []
def get_triples_for_entities(self, e1, e2):
try:
return self._kb_triples_by_entities[e1][e2]
except KeyError:
return []
def __repr__(self):
return 'KB with {} triples'.format(len(self._kb_triples))
kb = KB(kb_triples)
kb
KB with 56575 triples
Let's get a sense of the high-level characteristics of this KB. Some questions we'd like to answer:
all_relations = kb.get_all_relations()
print(len(all_relations))
16
How big is each relation? That is, how many triples does each relation contain?
for rel in all_relations:
print('{:12d} {}'.format(len(kb.get_triples_for_relation(rel)), rel))
2140 adjoins 3316 author 637 capital 22489 contains 4958 film_performance 2404 founders 1012 genre 3280 has_sibling 3774 has_spouse 3153 is_a 1981 nationality 2013 parents 1388 place_of_birth 1031 place_of_death 1526 profession 1473 worked_at
Let's look at one example from each relation, so that we can get a sense of what they mean.
for rel in all_relations:
print(tuple(kb.get_triples_for_relation(rel)[0]))
('adjoins', 'Siegburg', 'Bonn') ('author', 'Uncle_Silas', 'Sheridan_Le_Fanu') ('capital', 'Tunisia', 'Tunis') ('contains', 'Brickfields', 'Kuala_Lumpur_Sentral_railway_station') ('film_performance', 'Colin_Hanks', 'The_Great_Buck_Howard') ('founders', 'Bomis', 'Jimmy_Wales') ('genre', 'SPARQL', 'Semantic_Web') ('has_sibling', 'Ari_Emanuel', 'Rahm_Emanuel') ('has_spouse', 'Percy_Bysshe_Shelley', 'Mary_Shelley') ('is_a', 'Bhanu_Athaiya', 'Costume_designer') ('nationality', 'Ruben_Rausing', 'Sweden') ('parents', 'Prince_Arthur_of_Connaught', 'Prince_Arthur,_Duke_of_Connaught_and_Strathearn') ('place_of_birth', 'William_Penny_Brookes', 'Much_Wenlock') ('place_of_death', 'Jean_Drapeau', 'Montreal') ('profession', 'Rufus_Wainwright', 'Actor') ('worked_at', 'Ray_Jackendoff', 'Tufts_University')
The get_triples_for_entities()
method allows us to look up triples by the entities they contain. Let's use it to see what relation(s) hold between France
and Germany
.
kb.get_triples_for_entities('France', 'Germany')
[KBTriple(rel='adjoins', sbj='France', obj='Germany')]
Relations like adjoins
and has_sibling
are intuitively symmetric — if the relation holds between X and Y, then we expect it to hold between Y and X as well.
kb.get_triples_for_entities('Germany', 'France')
[KBTriple(rel='adjoins', sbj='Germany', obj='France')]
However, there's no guarantee that all such inverse triples actually appear in the KB. (You could write some code to check.)
Most relations, however, are intuitively asymmetric. Let's see what relation holds between Pixar
and Steve_Jobs
.
kb.get_triples_for_entities('Pixar', 'Steve_Jobs')
[KBTriple(rel='founders', sbj='Pixar', obj='Steve_Jobs')]
It's a bit arbitrary that the KB includes a given asymmetric relation rather than its inverse. For example, instead of the founders
relation with triple (founders, Pixar, Steve_Jobs)
, we might have had a founder_of
relation with triple (founder_of, Steve_Jobs, Pixar)
. It doesn't really matter.
Although we don't have a founder_of
relation, there might still be a relation between Steve_Jobs
and Pixar
. Let's check.
kb.get_triples_for_entities('Steve_Jobs', 'Pixar')
[KBTriple(rel='worked_at', sbj='Steve_Jobs', obj='Pixar')]
Aha, yes, that makes sense. So it can be the case that one relation holds between X and Y, and a different relation holds between Y and X.
One more observation: there may be more than one relation that holds between a given pair of entities, even in one direction.
kb.get_triples_for_entities('Cleopatra', 'Ptolemy_XIII_Theos_Philopator')
[KBTriple(rel='has_sibling', sbj='Cleopatra', obj='Ptolemy_XIII_Theos_Philopator'), KBTriple(rel='has_spouse', sbj='Cleopatra', obj='Ptolemy_XIII_Theos_Philopator')]
No! What? Yup, it's true — Cleopatra married her younger brother, Ptolemy XIII. Wait, it gets worse — she also married her even younger brother, Ptolemy XIV. Apparently this was normal behavior in ancient Egypt.
Moving on ...
Let's look at the distribution of entities in the KB. How many entities are there, and what are the most common ones?
counter = Counter()
for kbt in kb.get_triples():
counter[kbt.sbj] += 1
counter[kbt.obj] += 1
print('The KB contains {} entities'.format(len(counter)))
counts = sorted([(count, key) for key, count in counter.items()], reverse=True)
print('The most common entities are:')
for count, key in counts[:20]:
print('{:10d} {}'.format(count, key))
The KB contains 46275 entities The most common entities are: 962 England 815 India 465 London 456 Italy 437 France 420 Germany 412 California 396 United_Kingdom 378 Canada 324 New_York_City 262 Actor 248 New_York 244 Australia 235 China 226 Philippines 224 Japan 223 Russia 214 Scotland 204 Europe 177 Pakistan
The number of entities in the KB is less than half the number of entities in the corpus! Evidently the corpus has much broader coverage than the KB.
Note that there is no promise or expectation that this KB is complete. Not only does the KB contain no mention of many entities from the corpus — even for the entities it does include, there may be possible triples which are true in the world but are missing from the KB. As an example, these triples are in the KB:
(founders, SpaceX, Elon_Musk)
(founders, Tesla_Motors, Elon_Musk)
(worked_at, Elon_Musk, Tesla_Motors)
but this one is not:
(worked_at, Elon_Musk, SpaceX)
In fact, the whole point of developing methods for automatic relation extraction is to extend existing KBs (and build new ones) by identifying new relational triples from natural language text. If our KBs were complete, we wouldn't have anything to do.
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With our data assets in hand, it's time to provide a precise formulation of the prediction problem we aim to solve. We need to specify:
In order to leverage the distant supervision paradigm, we'll need to connect information in the corpus with information in the KB. There are two possibilities, depending on how we formulate our prediction problem:
So we'll formulate our prediction problem such that the input is a pair of entities, and the goal is to predict what relation(s) the pair belongs to. The KB will provide the labels, and the corpus will provide the features.
Let's determine how many examples we have for each triple in the KB. We'll compute averages per relation.
def count_examples(corpus, kb):
counter = Counter()
for rel in all_relations:
for kbt in kb.get_triples_for_relation(rel):
# count examples in both forward and reverse directions
counter[rel] += len(corpus.get_examples_for_entities(kbt.sbj, kbt.obj))
counter[rel] += len(corpus.get_examples_for_entities(kbt.obj, kbt.sbj))
# report results
print('{:20s} {:>10s} {:>10s} {:>10s}'.format('', '', '', 'examples'))
print('{:20s} {:>10s} {:>10s} {:>10s}'.format('relation', 'examples', 'triples', '/triple'))
print('{:20s} {:>10s} {:>10s} {:>10s}'.format('--------', '--------', '-------', '-------'))
for rel in all_relations:
nx = counter[rel]
nt = len(kb.get_triples_for_relation(rel))
print('{:20s} {:10d} {:10d} {:10.2f}'.format(rel, nx, nt, 1.0 * nx / nt))
count_examples(corpus, kb)
examples relation examples triples /triple -------- -------- ------- ------- adjoins 85660 2140 40.03 author 15822 3316 4.77 capital 12520 637 19.65 contains 99572 22489 4.43 film_performance 11195 4958 2.26 founders 8061 2404 3.35 genre 1941 1012 1.92 has_sibling 12332 3280 3.76 has_spouse 16188 3774 4.29 is_a 6955 3153 2.21 nationality 4649 1981 2.35 parents 5387 2013 2.68 place_of_birth 2214 1388 1.60 place_of_death 2047 1031 1.99 profession 2876 1526 1.88 worked_at 4494 1473 3.05
For most relations, the total number of examples is fairly large, so we can be optimistic about learning what linguistic patterns express a given relation. However, for individual entity pairs, the number of examples is often quite low. Of course, more data would be better — much better! But more data could quickly become unwieldy to work with in a notebook like this.
By joining the corpus to the KB, we can obtain abundant positive instances for each relation. But a classifier cannot be trained on positive instances alone. In order to apply the distant supervision paradigm, we will also need some negative instances — that is, entity pairs which do not belong to any known relation. If you like, you can think of these entity pairs as being assigned to a special relation called NO_RELATION
. We can find plenty of such pairs by searching for examples in the corpus which contain two entities which do not belong to any relation in the KB.
def find_unrelated_pairs(corpus, kb):
unrelated_pairs = set()
for ex in corpus.get_examples():
if kb.get_triples_for_entities(ex.entity_1, ex.entity_2):
continue
if kb.get_triples_for_entities(ex.entity_2, ex.entity_1):
continue
unrelated_pairs.add((ex.entity_1, ex.entity_2))
unrelated_pairs.add((ex.entity_2, ex.entity_1))
return unrelated_pairs
unrelated_pairs = find_unrelated_pairs(corpus, kb)
print('Found {} unrelated pairs, including:'.format(len(unrelated_pairs)))
for pair in list(unrelated_pairs)[:10]:
print(' ', pair)
Found 301073 unrelated pairs, including: ('Lainie_Kazan', 'Tab_Hunter') ('City_of_Brussels', 'Belgian_Comic_Strip_Center') ('John_Francis_Daley', 'Hart_Hanson') ('Andrew_W._Mellon_Foundation', 'American_Council_on_Education') ('Hollywood_Walk_of_Fame', 'Laura_Ingalls_Wilder_Medal') ('Keeping_the_Faith', 'Great_Expectations') ('American_Revolutionary_War', 'British_Empire') ('Sino-Indian_War', 'Bangladesh_Liberation_War') ('Greg_Howe', 'Richie_Kotzen') ('A41_road', 'Marble_Arch')
That's a lot of negative instances! In fact, because these negative instances far outnumber our positive instances (that is, the triples in our KB), when we train models we'll wind up downsampling the negative instances substantially.
Remember, though, that some of these supposedly negative instances may be false negatives. Our KB is not complete. A pair of entities might be related in real life even if they don't appear together in the KB.
A given pair of entities can belong to more than one relation. In fact, this is quite common in our KB.
def count_relation_combinations(kb):
counter = Counter()
for sbj, obj in kb.get_all_entity_pairs():
rels = tuple(sorted(set([kbt.rel for kbt in kb.get_triples_for_entities(sbj, obj)])))
if len(rels) > 1:
counter[rels] += 1
counts = sorted([(count, key) for key, count in counter.items()], reverse=True)
print('The most common relation combinations are:')
for count, key in counts:
print('{:10d} {}'.format(count, key))
count_relation_combinations(kb)
The most common relation combinations are: 1526 ('is_a', 'profession') 495 ('capital', 'contains') 183 ('place_of_birth', 'place_of_death') 76 ('nationality', 'place_of_birth') 11 ('nationality', 'place_of_death') 11 ('adjoins', 'contains') 8 ('has_sibling', 'has_spouse') 3 ('nationality', 'place_of_birth', 'place_of_death') 2 ('parents', 'worked_at') 1 ('nationality', 'worked_at') 1 ('has_spouse', 'parents') 1 ('author', 'founders')
While a few of those combinations look like data errors, most look natural and intuitive. Multiple relations per entity pair is a commonplace phenomenon.
This observation strongly suggests formulating our prediction problem as multi-label classification. We could instead treat it as multi-class classification — and indeed, Mintz et al. 2009 did so — but if we do, we'll be faced with the problem of assigning a single relation label to entity pairs which actually belong to multiple relations. It's not obvious how best to do this (and Mintz et al. 2009 did not make their method clear).
There are a number of ways to approach multi-label classification, but the most obvious is the binary relevance method, which just factors multi-label classification over n labels into n independent binary classification problems, one for each label. A disadvantage of this approach is that, by treating the binary classification problems as independent, it fails to exploit correlations between labels. But it has the great virtue of simplicity, and it will suffice for our purposes.
So our problem will be to take as input an entity pair and a candidate relation (label), and to return a binary prediction as to whether the entity pair belongs to the relation. Since a KB triple is precisely a relation and a pair of entities, we could say equivalently that our prediction problem amounts to binary classification of KB triples. Given a candidate KB triple, do we predict that it is valid?
We're now in a position to write a function to build datasets suitable for training and evaluating predictive models. It will have the following characteristics:
KBTriples
which combine the given relation with a pair of entities.KBTriple
belongs to the KB.KBTriples
derived from two sources:def build_datasets(corpus, kb, include_positive=True, sampling_rate=0.1, seed=1):
unrelated_pairs = find_unrelated_pairs(corpus, kb)
random.seed(seed)
unrelated_pairs = random.sample(unrelated_pairs, int(sampling_rate * len(unrelated_pairs)))
kbts_by_rel = defaultdict(list)
labels_by_rel = defaultdict(list)
for index, rel in enumerate(all_relations):
if include_positive:
for kbt in kb.get_triples_for_relation(rel):
kbts_by_rel[rel].append(kbt)
labels_by_rel[rel].append(True)
for sbj, obj in unrelated_pairs:
kbts_by_rel[rel].append(KBTriple(rel, sbj, obj))
labels_by_rel[rel].append(False)
return kbts_by_rel, labels_by_rel
[ top ]
Before we start building models, let's set up a test harness that allows us to measure a model's performance. This may seem backwards, but it's analogous to the software engineering paradigm of test-driven development: first, define success; then, pursue it.
Whenever building a model from data, it's good practice to partition the data into a multiple splits — minimally, a training split on which to train the model, and a test split on which to evaluate it. In fact, we'll go a bit further, and define four splits:
tiny
split (1%). It's often useful to carve out a tiny chunk of data to use in place of training or test data during development. Of course, any quantitative results obtained by evaluating on the tiny
split are nearly meaningless, but because evaluations run extremely fast, using this split is a good way to flush out bugs during iterative cycles of code development.train
split (69%). We'll use the majority of our data for training models, both during development and at final evaluation. Experiments with the train
split may take longer to run, but they'll have much greater statistical power.dev
split (15%). We'll use the dev
split as test data for intermediate (formative) evaluations during development. During routine experiments, all evaluations should use the dev
split.test
split (15%). We'll reserve the test
split for our final (summative) evaluation at the conclusion of our work. Running evaluations on the test
split before you are ready to conclude your work is methodologically unsound and intellectually dishonest!Splitting our data assets is somewhat more complicated than in many other NLP problems, because we have both a corpus and KB. In order to minimize leakage of information from training data into test data, we'd like to split both the corpus and the KB. And in order to maximize the value of a finite quantity of data, we'd like to align the corpus splits and KB splits as closely as possible. In an ideal world, each split would have its own hermetically-sealed universe of entities, the corpus for that split would contain only examples mentioning those entities, and the KB for that split would contain only triples involving those entities. However, that ideal is not quite achievable in practice. In order to get as close as possible, we'll follow this plan:
Here's code to implement the splits. It's OK to skip past the details.
def split_corpus_and_kb(
split_names=['tiny', 'train', 'dev', 'test'],
split_fracs=[0.01, 0.69, 0.15, 0.15],
seed=1):
if len(split_names) != len(split_fracs):
raise ValueError('split_names and split_fracs must be of equal length')
if sum(split_fracs) != 1.0:
raise ValueError('split_fracs must sum to 1')
n = len(split_fracs) # for convenience only
def split_list(xs):
xs = sorted(xs) # sorted for reproducibility
if seed:
random.seed(seed)
random.shuffle(xs)
split_points = [0] + [int(round(frac * len(xs))) for frac in np.cumsum(split_fracs)]
return [xs[split_points[i]:split_points[i + 1]] for i in range(n)]
# first, split the entities that appear as subjects in the KB
sbjs = list(set([kbt.sbj for kbt in kb.get_triples()]))
sbj_splits = split_list(sbjs)
sbj_split_dict = dict([(sbj, i) for i, split in enumerate(sbj_splits) for sbj in split])
# next, split the KB triples based on their subjects
kbt_splits = [[kbt for kbt in kb.get_triples() if sbj_split_dict[kbt.sbj] == i] for i in range(n)]
# now split examples based on the entities they contain
ex_splits = [[] for i in range(n + 1)] # include an extra split
for ex in corpus.get_examples():
if ex.entity_1 in sbj_split_dict:
# if entity_1 is a sbj in the KB, assign example to split of that sbj
ex_splits[sbj_split_dict[ex.entity_1]].append(ex)
elif ex.entity_2 in sbj_split_dict:
# if entity_2 is a sbj in the KB, assign example to split of that sbj
ex_splits[sbj_split_dict[ex.entity_2]].append(ex)
else:
# otherwise, put in extra split to be redistributed
ex_splits[-1].append(ex)
# reallocate the examples that weren't assigned to a split on first pass
extra_ex_splits = split_list(ex_splits[-1])
ex_splits = [ex_splits[i] + extra_ex_splits[i] for i in range(n)]
# create a Corpus and a KB for each split
data = {}
for i in range(n):
data[split_names[i]] = {'corpus': Corpus(ex_splits[i]), 'kb': KB(kbt_splits[i])}
data['all'] = {'corpus': corpus, 'kb': kb}
return data
Great. Let's use it to create the splits.
data = split_corpus_and_kb(seed=1)
data
{'all': {'corpus': Corpus with 414123 examples, 'kb': KB with 56575 triples}, 'dev': {'corpus': Corpus with 57241 examples, 'kb': KB with 7939 triples}, 'test': {'corpus': Corpus with 63382 examples, 'kb': KB with 8053 triples}, 'tiny': {'corpus': Corpus with 3458 examples, 'kb': KB with 425 triples}, 'train': {'corpus': Corpus with 290042 examples, 'kb': KB with 40158 triples}}
So now we can use data['train']['corpus']
to refer to the training corpus, or data['dev']['kb']
to refer to the dev KB.
As a convenience, let's add a function for creating datasets for a specific split:
def build_datasets_for_split(split, include_positive=True, sampling_rate=0.1, seed=1):
return build_datasets(data[split]['corpus'], data[split]['kb'], include_positive, sampling_rate, seed)
Because we've formulated our prediction problem as a family of binary classification problems, one for each relation (label), choosing evaluation metrics is pretty straightforward. The standard metrics for evaluating binary classification are precision and recall, which are more meaningful than simple accuracy, particularly in problems with a highly biased label distribution (like ours). We'll compute and report precision and recall separately for each relation (label). There are only two wrinkles:
How best to combine precision and recall into a single metric. Having two evaluation metrics is often inconvenient. If we're considering a change to our model which improves precision but degrades recall, should we take it? To drive an iterative development process, it's useful to have a single metric on which to hill-climb. For binary classification, the standard answer is the F1-score, which is the harmonic mean of precision and recall. However, the F1-score gives equal weight to precision and recall. For our purposes, precision is probably more important than recall. If we're extracting new relation triples from (massively abundant) text on the web in order to augment a knowledge base, it's probably more important that the triples we extract are correct (precision) than that we extract all the triples we could (recall). Accordingly, instead of the F1-score, we'll use the F0.5-score, which gives precision twice as much weight as recall.
How to aggregate metrics across relations (labels). Reporting metrics separately for each relation is great, but in order to drive iterative development, we'd also like to have summary metrics which aggregate across all relations. There are two possible ways to do it: micro-averaging will give equal weight to all problem instances, and thus give greater weight to relations with more instances, while macro-averaging will give equal weight to all relations, and thus give lesser weight to problem instances in relations with more instances. Because the number of problem instances per relation is, to some degree, an accident of our data collection methodology, we'll choose macro-averaging.
Thus, while every evaluation will report lots of metrics, when we need a single metric on which to hill-climb, it will be the macro-averaged F0.5-score.
It's time to write some code to run evaluations and report results. This is now straightforward. The evaluate()
function takes as inputs:
classifier
, which is just a function that takes a list of KBTriples
and returns a list of boolean predictions;test_split
, the split on which to evaluate the classifier, dev
by default;verbose
, a boolean indicating whether to print output.The other functions below are just helper functions to evaluate()
.
def print_statistics_header():
print('{:20s} {:>10s} {:>10s} {:>10s} {:>10s} {:>10s}'.format(
'relation', 'precision', 'recall', 'f-score', 'support', 'size'))
print('{:20s} {:>10s} {:>10s} {:>10s} {:>10s} {:>10s}'.format(
'-' * 18, '-' * 9, '-' * 9, '-' * 9, '-' * 9, '-' * 9))
def print_statistics_row(rel, result):
print('{:20s} {:10.3f} {:10.3f} {:10.3f} {:10d} {:10d}'.format(rel, *result))
def print_statistics_footer(avg_result):
print('{:20s} {:>10s} {:>10s} {:>10s} {:>10s} {:>10s}'.format(
'-' * 18, '-' * 9, '-' * 9, '-' * 9, '-' * 9, '-' * 9))
print('{:20s} {:10.3f} {:10.3f} {:10.3f} {:10d} {:10d}'.format('macro-average', *avg_result))
def macro_average_results(results):
avg_result = [np.average([r[i] for r in results.values()]) for i in range(3)]
avg_result.append(np.sum([r[3] for r in results.values()]))
avg_result.append(np.sum([r[4] for r in results.values()]))
return avg_result
def evaluate(classifier, test_split='dev', verbose=True):
test_kbts_by_rel, true_labels_by_rel = build_datasets_for_split(test_split)
results = {}
if verbose:
print_statistics_header()
for rel in all_relations:
pred_labels = classifier(test_kbts_by_rel[rel])
stats = precision_recall_fscore_support(true_labels_by_rel[rel], pred_labels, beta=0.5)
stats = [stat[1] for stat in stats] # stats[1] is the stat for label True
stats.append(len(pred_labels)) # number of examples
results[rel] = stats
if verbose:
print_statistics_row(rel, results[rel])
avg_result = macro_average_results(results)
if verbose:
print_statistics_footer(avg_result)
return avg_result[2] # return f_0.5 score as summary statistic
In order to validate our evaluation framework, and to set a floor under expected results for future evaluations, let's implement and evaluate a random-guessing strategy. The random guesser is a classifier which completely ignores its input, and simply flips a coin.
def lift(f):
return lambda xs: [f(x) for x in xs]
def make_random_classifier(p=0.50):
def random_classify(kb_triple):
return random.random() < p
return lift(random_classify)
evaluate(make_random_classifier())
relation precision recall f-score support size ------------------ --------- --------- --------- --------- --------- adjoins 0.058 0.515 0.070 303 5319 author 0.088 0.508 0.106 480 5496 capital 0.019 0.539 0.024 89 5105 contains 0.349 0.502 0.371 2667 7683 film_performance 0.138 0.491 0.162 822 5838 founders 0.064 0.482 0.078 359 5375 genre 0.039 0.608 0.048 166 5182 has_sibling 0.092 0.493 0.109 513 5529 has_spouse 0.110 0.530 0.130 575 5591 is_a 0.099 0.547 0.119 494 5510 nationality 0.054 0.463 0.066 311 5327 parents 0.062 0.502 0.075 325 5341 place_of_birth 0.040 0.488 0.049 217 5233 place_of_death 0.028 0.490 0.034 145 5161 profession 0.052 0.563 0.063 245 5261 worked_at 0.047 0.544 0.058 228 5244 ------------------ --------- --------- --------- --------- --------- macro-average 0.084 0.517 0.098 7939 88195
0.09757501010273492
The results are not too surprising. Recall is generally around 0.50, which makes sense: on any given example with label True
, we are 50% likely to guess the right label. But precision is very poor, because most labels are not True
, and because our classifier is completely ignorant of the features of specific problem instances. Accordingly, the F0.5-score is also very poor — first because even the equally-weighted F1-score is always closer to the lesser of precision and recall, and second because the F0.5-score weights precision twice as much as recall.
Actually, the most remarkable result in this table is the comparatively good performance for the contains
relation! What does this result tell us about the data?
[ top ]
It shouldn't be too hard to do better than random guessing. But for now, let's aim low — let's use the data we have in the easiest and most obvious way, and see how far that gets us.
We start from the intuition that the words between two entity mentions frequently tell us how they're related. For example, in the phrase "SpaceX was founded by Elon Musk", the words "was founded by" indicate that the founders
relation holds between the first entity mentioned and the second. Likewise, in the phrase "Elon Musk established SpaceX", the word "established" indicates the founders
relation holds between the second entity mentioned and the first.
So let's write some code to find the most common phrases that appear between the two entity mentions for each relation. As the examples illustrate, we need to make sure to consider both directions: that is, where the subject of the relation appears as the first mention, and where it appears as the second.
def find_common_middles(split='train', top_k=3, show_output=False):
corpus = data[split]['corpus']
kb = data[split]['kb']
mids_by_rel = {
'fwd': defaultdict(lambda: defaultdict(int)),
'rev': defaultdict(lambda: defaultdict(int)),
}
for rel in all_relations:
for kbt in kb.get_triples_for_relation(rel):
for ex in corpus.get_examples_for_entities(kbt.sbj, kbt.obj):
mids_by_rel['fwd'][rel][ex.middle] += 1
for ex in corpus.get_examples_for_entities(kbt.obj, kbt.sbj):
mids_by_rel['rev'][rel][ex.middle] += 1
def most_frequent(mid_counter):
return sorted([(cnt, mid) for mid, cnt in mid_counter.items()], reverse=True)[:top_k]
for rel in all_relations:
for dir in ['fwd', 'rev']:
top = most_frequent(mids_by_rel[dir][rel])
if show_output:
for cnt, mid in top:
print('{:20s} {:5s} {:10d} {:s}'.format(rel, 'fwd', cnt, mid))
mids_by_rel[dir][rel] = set([mid for cnt, mid in top])
return mids_by_rel
_ = find_common_middles(show_output=True)
adjoins fwd 8461 , adjoins fwd 5633 and adjoins fwd 993 , and adjoins fwd 5599 , adjoins fwd 3780 and adjoins fwd 680 , and author fwd 1214 by author fwd 155 , author fwd 130 , by author fwd 1106 's author fwd 294 ‘ s author fwd 175 ’ s capital fwd 37 , capital fwd 19 in capital fwd 18 ( capital fwd 3711 , capital fwd 178 in capital fwd 87 , the capital of contains fwd 460 's contains fwd 355 , contains fwd 250 ( contains fwd 25095 , contains fwd 5603 in contains fwd 668 in the film_performance fwd 286 in film_performance fwd 200 's film_performance fwd 115 film film_performance fwd 213 with film_performance fwd 152 , starring film_performance fwd 115 opposite founders fwd 98 founder founders fwd 59 co-founder founders fwd 57 , founders fwd 180 's founders fwd 104 of founders fwd 77 ‘ s genre fwd 28 , a genre fwd 13 in 1994 , he became a central figure in the genre fwd 11 is a genre fwd 122 , genre fwd 62 series genre fwd 23 has_sibling fwd 1369 and has_sibling fwd 614 , has_sibling fwd 139 , and has_sibling fwd 930 and has_sibling fwd 460 , has_sibling fwd 94 , and has_spouse fwd 2029 and has_spouse fwd 375 , has_spouse fwd 112 and his wife has_spouse fwd 1382 and has_spouse fwd 271 , has_spouse fwd 78 and his wife is_a fwd 120 , is_a fwd 77 and is_a fwd 34 , a is_a fwd 252 , is_a fwd 175 and is_a fwd 81 nationality fwd 331 of nationality fwd 79 in nationality fwd 34 of the nationality fwd 57 , nationality fwd 27 by nationality fwd 25 under parents fwd 77 , son of parents fwd 50 and parents fwd 44 , parents fwd 177 and parents fwd 167 , parents fwd 47 and his son place_of_birth fwd 90 of place_of_birth fwd 64 was born in place_of_birth fwd 37 in place_of_birth fwd 17 , place_of_birth fwd 16 by place_of_birth fwd 11 under place_of_death fwd 73 in place_of_death fwd 63 of place_of_death fwd 17 at place_of_death fwd 12 , place_of_death fwd 10 under place_of_death fwd 9 mayor profession fwd 65 , profession fwd 27 , a profession fwd 20 and profession fwd 114 , profession fwd 74 profession fwd 24 and worked_at fwd 103 of worked_at fwd 82 at worked_at fwd 66 's worked_at fwd 37 , worked_at fwd 30 founder worked_at fwd 25 co-founder
A few observations here:
parents
relation, while "and his son" indicates a reverse parents
relation.genre
relation reflects both the relative scarcity of examples for that relation, and an unfortunate tendency of the Wikilinks dataset to include duplicate or near-duplicate source documents. (That middle connects the entities Ready to Die — the first studio album by the Notorious B.I.G. — and East Coast hip hop.)def train_top_k_middles_classifier(train_split='train', top_k=3):
corpus = data[train_split]['corpus']
top_k_mids_by_rel = find_common_middles(split=train_split, top_k=top_k)
def classify(kb_triple):
fwd_mids = top_k_mids_by_rel['fwd'][kb_triple.rel]
rev_mids = top_k_mids_by_rel['rev'][kb_triple.rel]
for ex in corpus.get_examples_for_entities(kb_triple.sbj, kb_triple.obj):
if ex.middle in fwd_mids:
return True
for ex in corpus.get_examples_for_entities(kb_triple.obj, kb_triple.sbj):
if ex.middle in rev_mids:
return True
return False
return lift(classify)
evaluate(train_top_k_middles_classifier())
relation precision recall f-score support size ------------------ --------- --------- --------- --------- --------- adjoins 0.311 0.406 0.327 303 5319 author 0.212 0.058 0.139 480 5496 capital 0.087 0.191 0.098 89 5105 contains 0.494 0.066 0.214 2667 7683 film_performance 0.250 0.002 0.012 822 5838 founders 0.177 0.061 0.129 359 5375 genre 0.000 0.000 0.000 166 5182 has_sibling 0.295 0.222 0.277 513 5529 has_spouse 0.359 0.249 0.330 575 5591 is_a 0.019 0.010 0.016 494 5510 nationality 0.115 0.039 0.083 311 5327 parents 0.079 0.068 0.077 325 5341 place_of_birth 0.052 0.023 0.042 217 5233 place_of_death 0.011 0.007 0.010 145 5161 profession 0.008 0.008 0.008 245 5261 worked_at 0.074 0.031 0.058 228 5244 ------------------ --------- --------- --------- --------- --------- macro-average 0.159 0.090 0.114 7939 88195
0.11360108865631796
Not surprisingly, the performance of even this extremely simplistic model is noticeably better than random guessing. Of course, recall is much worse across the board, but precision and F0.5-score are sometimes much better. We observe big gains especially on adjoins
, author
, contains
, founders
, has_sibling
, and has spouse
. Then again, at least one relation actually got worse. (Can you offer any explanation for that?)
Admittedly, performance is still not great in absolute terms. However, we should have modest expectations for performance on this task — we are unlikely ever to get anywhere near perfect precision with perfect recall. Why?
Because of these unavoidable obstacles, what matters is not so much absolute performance, but relative performance of different approaches.
Exercise: What's the optimal value for top_k
, the number of most frequent middles to consider? What choice maximizes our chosen figure of merit, the macro-averaged F0.5-score?
[ top ]
OK, it's time to get (halfway) serious. Let's apply real machine learning to train a classifier on the training data, and see how it performs on the test data. We'll begin with one of the simplest machine learning setups: a bag-of-words feature representation, and a linear model trained using logistic regression.
Just like we did in the unit on supervised sentiment analysis, we'll leverage the sklearn
library, and we'll introduce functions for featurizing instances, training models, making predictions, and evaluating results.
Featurizers are functions which define the feature representation for our model. The primary input to a featurizer will be the KBTriple
for which we are generating features. But since our features will be derived from corpus examples containing the entities of the KBTriple
, we must also pass in a reference to a Corpus
. And in order to make it easy to combine different featurizers, we'll also pass in a feature counter to hold the results.
Here's an implementation for a very simple bag-of-words featurizer. It finds all the corpus examples containing the two entities in the KBTriple
, breaks the phrase appearing between the two entity mentions into words, and counts the words. Note that it makes no distinction between "forward" and "reverse" examples.
def simple_bag_of_words_featurizer(kbt, corpus, feature_counter):
for ex in corpus.get_examples_for_entities(kbt.sbj, kbt.obj):
for word in ex.middle.split(' '):
feature_counter[word] += 1
for ex in corpus.get_examples_for_entities(kbt.obj, kbt.sbj):
for word in ex.middle.split(' '):
feature_counter[word] += 1
You can experiment with adding new kinds of features just by implementing additional featurizers, following simple_bag_of_words_featurizer
as an example.
Now, in order to apply machine learning algorithms such as those provided by sklearn
, we need a way to convert datasets of KBTriple
s into feature matrices. The function featurize_datasets()
achieves that. It takes in a collection of KBTriple
s grouped by relation, and returns a corresponding collection of feature matrices grouped by relation. It also needs a Corpus
from which to extract features, and a list of featurizers to generate the features. Finally, it accepts a vectorizer as an optional argument. At training time, we won't supply a vectorizer, so this code will create a new one; at test time, we'll supply the vectorizer we created at training time.
def featurize_datasets(
kbts_by_rel,
corpus,
featurizers=[simple_bag_of_words_featurizer],
vectorizer=None):
# Create feature counters for all instances (kbts).
feat_counters_by_rel = defaultdict(list)
for rel, kbts in kbts_by_rel.items():
for kbt in kbts:
feature_counter = Counter()
for featurizer in featurizers:
featurizer(kbt, corpus, feature_counter)
feat_counters_by_rel[rel].append(feature_counter)
feat_matrices_by_rel = defaultdict(list)
# If we haven't been given a Vectorizer, create one and fit it to all the feature counters.
if vectorizer == None:
vectorizer = DictVectorizer(sparse=True)
def traverse_dicts():
for dict_list in feat_counters_by_rel.values():
for d in dict_list:
yield d
vectorizer.fit(traverse_dicts())
# Now use the Vectorizer to transform feature dictionaries into feature matrices.
for rel, feat_counters in feat_counters_by_rel.items():
feat_matrices_by_rel[rel] = vectorizer.transform(feat_counters)
return feat_matrices_by_rel, vectorizer
Now we need some functions to train models, make predictions, and evaluate the results. We'll start with train_models()
. This function takes as arguments a data split on which to train, a list of featurizers, and model factory, which is a function which initializes an sklearn
classifier. It returns a dictionary holding the featurizers, the vectorizer that was used to generate the training matrix, and a dictionary holding the trained models, one per relation.
def train_models(
split='train',
featurizers=[simple_bag_of_words_featurizer],
model_factory=lambda: LogisticRegression(fit_intercept=True),
verbose=True):
if verbose: print('Building datasets')
train_o, train_y = build_datasets_for_split(split=split)
if verbose: print('Featurizing')
train_X, vectorizer = featurize_datasets(train_o, data[split]['corpus'], featurizers)
models = {}
if verbose: print('Training models')
for rel in all_relations:
models[rel] = model_factory()
models[rel].fit(train_X[rel], train_y[rel])
if verbose: print('Training complete\n')
return {
'featurizers': featurizers,
'vectorizer': vectorizer,
'models': models,
}
Next comes predict()
. This function takes as arguments a test split, a list of featurizers, the vectorizer that was used during training, and a dictionary holding the models, one per relation. It returns two parallel dictionaries: one holding the predictions (grouped by relation), the other holding the true labels (again, grouped by prediction).
def predict(split, featurizers, vectorizer, models):
test_o, test_y = build_datasets_for_split(split=split)
test_X, _ = featurize_datasets(test_o, data[split]['corpus'], featurizers, vectorizer=vectorizer)
predictions = {}
for rel in all_relations:
predictions[rel] = models[rel].predict(test_X[rel])
return predictions, test_y
Now evaluate_predictions()
. This function takes as arguments the parallel dictionaries of predictions and true labels produced by predict()
. It prints summary statistics for each relation, including precision, recall, and F0.5-score, and it returns the macro-averaged F0.5-score.
def evaluate_predictions(predictions, test_y, verbose=True):
results = {} # one result row for each relation
if verbose:
print_statistics_header()
for rel in all_relations:
stats = precision_recall_fscore_support(test_y[rel], predictions[rel], beta=0.5)
stats = [stat[1] for stat in stats] # stats[1] is the stat for label True
stats.append(len(test_y[rel]))
results[rel] = stats
if verbose:
print_statistics_row(rel, results[rel])
avg_result = macro_average_results(results)
if verbose:
print_statistics_footer(avg_result)
return avg_result[2] # return f_0.5 score as summary statistic
Finally, we introduce experiment()
, which simply chains together train_models()
, predict()
, and evaluate_predictions()
. For convenience, this function returns the output of train_models()
as its result.
def experiment(
train_split='train',
test_split='dev',
featurizers=[simple_bag_of_words_featurizer],
model_factory=lambda: LogisticRegression(fit_intercept=True),
verbose=True):
train_result = train_models(train_split, featurizers, model_factory, verbose)
predictions, test_y = predict(test_split,
featurizers,
train_result['vectorizer'],
train_result['models'])
evaluate_predictions(predictions, test_y, verbose)
return train_result
Running experiment()
in its default configuration will give us a baseline result for machine-learned models.
_ = experiment()
# _ = experiment(train_split='tiny', test_split='tiny') # better for rapid development
Building datasets Featurizing Training models Training complete relation precision recall f-score support size ------------------ --------- --------- --------- --------- --------- adjoins 0.874 0.459 0.740 303 5319 author 0.830 0.558 0.756 480 5496 capital 0.677 0.236 0.493 89 5105 contains 0.752 0.615 0.720 2667 7683 film_performance 0.820 0.580 0.757 822 5838 founders 0.837 0.387 0.679 359 5375 genre 0.619 0.235 0.467 166 5182 has_sibling 0.870 0.234 0.563 513 5529 has_spouse 0.900 0.362 0.694 575 5591 is_a 0.710 0.223 0.494 494 5510 nationality 0.584 0.145 0.363 311 5327 parents 0.877 0.569 0.791 325 5341 place_of_birth 0.759 0.203 0.490 217 5233 place_of_death 0.621 0.124 0.345 145 5161 profession 0.639 0.159 0.399 245 5261 worked_at 0.740 0.250 0.532 228 5244 ------------------ --------- --------- --------- --------- --------- macro-average 0.757 0.334 0.580 7939 88195
Considering how vanilla our model is, these results are quite surprisingly good! We see huge gains for every relation over our top_k_middles_classifier
. This strong performance is a powerful testament to the effectiveness of even the simplest forms of machine learning.
But there is still much more we can do. To make further gains, we must not treat the model as a black box. We must open it up and get visibility into what it has learned, and more importantly, where it still falls down.
One important way to gain understanding of our trained model is to inspect the model weights. What features are strong positive indicators for each relation, and what features are strong negative indicators?
def examine_model_weights(
train_split='train',
featurizers=[simple_bag_of_words_featurizer],
model_factory=lambda: LogisticRegression(fit_intercept=True),
k=3,
verbose=True):
train_result = train_models(train_split, featurizers, model_factory, verbose)
feature_names = train_result['vectorizer'].get_feature_names()
for rel, model in train_result['models'].items():
print('Highest and lowest feature weights for relation {}:\n'.format(rel))
sorted_weights = sorted([(wgt, idx) for idx, wgt in enumerate(model.coef_[0])], reverse=True)
for wgt, idx in sorted_weights[:k]:
print('{:10.3f} {}'.format(wgt, feature_names[idx]))
print('{:>10s} {}'.format('.....', '.....'))
for wgt, idx in sorted_weights[-k:]:
print('{:10.3f} {}'.format(wgt, feature_names[idx]))
print()
examine_model_weights()
# examine_model_weights(train_split='tiny') # better for rapid development
Building datasets Featurizing Training models Training complete Highest and lowest feature weights for relation adjoins: 2.523 Córdoba 2.255 Taluks 2.001 nearby ..... ..... -1.193 an -1.338 Egypt -1.484 Caribbean Highest and lowest feature weights for relation author: 3.329 author 2.548 poem 2.463 wrote ..... ..... -2.312 directed -2.563 controversial -4.219 1945 Highest and lowest feature weights for relation capital: 3.833 capital 1.749 headquarters 1.735 towns ..... ..... -1.635 during -1.751 includes -1.963 Westminster Highest and lowest feature weights for relation contains: 2.517 third-largest 2.434 Channel 2.308 districts ..... ..... -2.316 rise -3.798 Ceylon -4.005 occupation Highest and lowest feature weights for relation film_performance: 4.141 starring 3.631 alongside 3.419 opposite ..... ..... -1.874 Khakee -1.994 Westminster -3.850 Mohabbatein Highest and lowest feature weights for relation founders: 3.969 founder 3.888 founded 2.649 company ..... ..... -1.682 William -1.893 writing -1.998 Griffith Highest and lowest feature weights for relation genre: 3.504 3.184 series 2.783 album ..... ..... -1.387 and -1.465 Playhouse -1.801 at Highest and lowest feature weights for relation has_sibling: 5.262 brother 4.058 sister 3.076 Marlon ..... ..... -1.463 starring -1.959 formed -2.062 Her Highest and lowest feature weights for relation has_spouse: 5.185 wife 4.286 husband 4.216 married ..... ..... -1.634 engineer -2.341 Straus -2.341 Isidor Highest and lowest feature weights for relation is_a: 4.043 stage 3.838 2.800 theatre ..... ..... -1.485 Texas -1.557 at -5.974 characin Highest and lowest feature weights for relation nationality: 2.586 born 2.035 -born 1.933 President ..... ..... -1.584 or -1.790 foreign -1.900 state Highest and lowest feature weights for relation parents: 5.083 daughter 4.963 son 4.377 father ..... ..... -1.374 need -1.477 no -2.788 Indian Highest and lowest feature weights for relation place_of_birth: 3.774 born 2.971 mayor 2.432 -born ..... ..... -1.371 and -1.447 or -1.762 Indian Highest and lowest feature weights for relation place_of_death: 2.745 died 2.228 assassinated 1.930 Germany ..... ..... -1.180 Belgium -1.433 state -1.953 Westminster Highest and lowest feature weights for relation profession: 4.132 2.762 American 2.272 philosopher ..... ..... -1.423 Texas -1.425 on -1.439 from Highest and lowest feature weights for relation worked_at: 3.425 professor 2.792 president 2.773 CEO ..... ..... -1.507 confluence -1.613 state -1.718 or
By and large, the high-weight features for each relation are pretty intuitive — they are words that are used to express the relation in question. (The counter-intuitive results merit a bit of investigation!)
The low-weight features (that is, features with large negative weights) may be a bit harder to understand. In some cases, however, they can be interpreted as features which indicate some other relation which is anti-correlated with the target relation. (As an example, "directed" is a negative indicator for the author
relation.)
Exercise: Investigate one of the counter-intuitive high-weight features. Find the training examples which caused the feature to be included. Given the training data, does it make sense that this feature is a good predictor for the target relation?
Another way to gain insight into our trained models is to use them to discover new relation instances that don't currently appear in the KB. In fact, this is the whole point of building a relation extraction system: to extend an existing KB (or build a new one) using knowledge extracted from natural language text at scale. Can the models we've trained do this effectively?
Because the goal is to discover new relation instances which are true but absent from the KB, we can't evalute this capability automatically. But we can generate candidate KB triples and manually evaluate them for correctness.
To do this, we'll start from corpus examples containing pairs of entities which do not belong to any relation in the KB (earlier, we described these as "negative examples"). We'll then apply our trained models to each pair of entities, and sort the results by probability assigned by the model, in order to find the most likely new instances for each relation.
def find_new_relation_instances(
train_split='train',
test_split='dev',
featurizers=[simple_bag_of_words_featurizer],
model_factory=lambda: LogisticRegression(fit_intercept=True),
k=10,
verbose=True):
# train models
train_result = train_models(train_split, featurizers, model_factory, verbose)
# build datasets for negative instances only
neg_o, neg_y = build_datasets_for_split(test_split, include_positive=False, sampling_rate=1.0)
neg_X, _ = featurize_datasets(neg_o,
data[test_split]['corpus'],
featurizers,
train_result['vectorizer'])
# report highest confidence predictions
for rel, model in train_result['models'].items():
print('Highest probability examples for relation {}:\n'.format(rel))
probs = model.predict_proba(neg_X[rel])
probs = [prob[1] for prob in probs] # probability for class True
sorted_probs = sorted([(p, idx) for idx, p in enumerate(probs)], reverse=True)
for p, idx in sorted_probs[:k]:
print('{:10.3f} {}'.format(p, neg_o[rel][idx]))
print()
find_new_relation_instances()
# find_new_relation_instances(train_split='tiny', test_split='tiny') # for rapid development
Building datasets Featurizing Training models Training complete Highest probability examples for relation adjoins: 1.000 KBTriple(rel='adjoins', sbj='Sun', obj='Moon') 1.000 KBTriple(rel='adjoins', sbj='Moon', obj='Sun') 1.000 KBTriple(rel='adjoins', sbj='India', obj='Maharashtra') 1.000 KBTriple(rel='adjoins', sbj='Maharashtra', obj='India') 1.000 KBTriple(rel='adjoins', sbj='Europe', obj='Great_Britain') 1.000 KBTriple(rel='adjoins', sbj='Great_Britain', obj='Europe') 1.000 KBTriple(rel='adjoins', sbj='Isle_of_Wight', obj='Ryde') 1.000 KBTriple(rel='adjoins', sbj='Ryde', obj='Isle_of_Wight') 1.000 KBTriple(rel='adjoins', sbj='Uttar_Pradesh', obj='India') 1.000 KBTriple(rel='adjoins', sbj='India', obj='Uttar_Pradesh') Highest probability examples for relation author: 1.000 KBTriple(rel='author', sbj='Systema_Naturae', obj='Carl_Linnaeus') 1.000 KBTriple(rel='author', sbj='The_Doors_of_Perception', obj='Aldous_Huxley') 1.000 KBTriple(rel='author', sbj='Aldous_Huxley', obj='The_Doors_of_Perception') 1.000 KBTriple(rel='author', sbj='Carl_Linnaeus', obj='Systema_Naturae') 1.000 KBTriple(rel='author', sbj='Charlie_and_the_Chocolate_Factory', obj='Roald_Dahl') 1.000 KBTriple(rel='author', sbj='Roald_Dahl', obj='Charlie_and_the_Chocolate_Factory') 1.000 KBTriple(rel='author', sbj='Stephen_Hawking', obj='A_Brief_History_of_Time') 1.000 KBTriple(rel='author', sbj='A_Brief_History_of_Time', obj='Stephen_Hawking') 1.000 KBTriple(rel='author', sbj='Neil_Gaiman', obj='American_Gods') 1.000 KBTriple(rel='author', sbj='American_Gods', obj='Neil_Gaiman') Highest probability examples for relation capital: 1.000 KBTriple(rel='capital', sbj='Italy', obj='Rome') 1.000 KBTriple(rel='capital', sbj='Rome', obj='Italy') 1.000 KBTriple(rel='capital', sbj='Isle_of_Wight', obj='Ryde') 1.000 KBTriple(rel='capital', sbj='Ryde', obj='Isle_of_Wight') 1.000 KBTriple(rel='capital', sbj='India', obj='Maharashtra') 1.000 KBTriple(rel='capital', sbj='Maharashtra', obj='India') 1.000 KBTriple(rel='capital', sbj='Chernobyl_Nuclear_Power_Plant', obj='Ukraine') 1.000 KBTriple(rel='capital', sbj='Ukraine', obj='Chernobyl_Nuclear_Power_Plant') 1.000 KBTriple(rel='capital', sbj='Blarney', obj='Republic_of_Ireland') 1.000 KBTriple(rel='capital', sbj='Republic_of_Ireland', obj='Blarney') Highest probability examples for relation contains: 1.000 KBTriple(rel='contains', sbj='Italy', obj='Rome') 1.000 KBTriple(rel='contains', sbj='Uttar_Pradesh', obj='India') 1.000 KBTriple(rel='contains', sbj='Isle_of_Wight', obj='Ryde') 1.000 KBTriple(rel='contains', sbj='India', obj='Maharashtra') 1.000 KBTriple(rel='contains', sbj='Roman_Empire', obj='Rome') 1.000 KBTriple(rel='contains', sbj='Rome', obj='Italy') 1.000 KBTriple(rel='contains', sbj='India', obj='Uttar_Pradesh') 1.000 KBTriple(rel='contains', sbj='Rome', obj='Roman_Empire') 1.000 KBTriple(rel='contains', sbj='Ryde', obj='Isle_of_Wight') 1.000 KBTriple(rel='contains', sbj='Maharashtra', obj='India') Highest probability examples for relation film_performance: 1.000 KBTriple(rel='film_performance', sbj='Hong_Kong', obj='Shanghai_Noon') 1.000 KBTriple(rel='film_performance', sbj='Shanghai_Noon', obj='Hong_Kong') 1.000 KBTriple(rel='film_performance', sbj='Francis_Ford_Coppola', obj='Robin_Williams') 1.000 KBTriple(rel='film_performance', sbj='Robin_Williams', obj='Francis_Ford_Coppola') 1.000 KBTriple(rel='film_performance', sbj='The_Pink_Panther_2', obj='Harald_Zwart') 1.000 KBTriple(rel='film_performance', sbj='Harald_Zwart', obj='The_Pink_Panther_2') 1.000 KBTriple(rel='film_performance', sbj='Salman_Khan', obj='Tere_Naam') 1.000 KBTriple(rel='film_performance', sbj='Tere_Naam', obj='Salman_Khan') 1.000 KBTriple(rel='film_performance', sbj='Gia', obj='Angelina_Jolie') 1.000 KBTriple(rel='film_performance', sbj='Angelina_Jolie', obj='Gia') Highest probability examples for relation founders: 1.000 KBTriple(rel='founders', sbj='L._Ron_Hubbard', obj='Church_of_Scientology') 1.000 KBTriple(rel='founders', sbj='Church_of_Scientology', obj='L._Ron_Hubbard') 1.000 KBTriple(rel='founders', sbj='Insect', obj='Lepidoptera') 1.000 KBTriple(rel='founders', sbj='Lepidoptera', obj='Insect') 1.000 KBTriple(rel='founders', sbj='Illuminati', obj='Adam_Weishaupt') 1.000 KBTriple(rel='founders', sbj='Adam_Weishaupt', obj='Illuminati') 1.000 KBTriple(rel='founders', sbj='Austria', obj='Gaston_Glock') 1.000 KBTriple(rel='founders', sbj='Gaston_Glock', obj='Austria') 1.000 KBTriple(rel='founders', sbj='Sri_Lanka', obj='Matale_District') 1.000 KBTriple(rel='founders', sbj='Matale_District', obj='Sri_Lanka') Highest probability examples for relation genre: 1.000 KBTriple(rel='genre', sbj='Cartoon_Cartoons', obj="Dexter's_Laboratory") 1.000 KBTriple(rel='genre', sbj="Dexter's_Laboratory", obj='Cartoon_Cartoons') 1.000 KBTriple(rel='genre', sbj='All_We_Know_Is_Falling', obj='Taylor_York') 1.000 KBTriple(rel='genre', sbj='Taylor_York', obj='All_We_Know_Is_Falling') 1.000 KBTriple(rel='genre', sbj='Lanner_falcon', obj='Falcon') 1.000 KBTriple(rel='genre', sbj='Falcon', obj='Lanner_falcon') 0.998 KBTriple(rel='genre', sbj='Meg_Griffin', obj='Family_Guy') 0.998 KBTriple(rel='genre', sbj='Family_Guy', obj='Meg_Griffin') 0.982 KBTriple(rel='genre', sbj='Tattoo_artist', obj='Tattoo_artist') 0.977 KBTriple(rel='genre', sbj='Tunch_Ilkin', obj='Tunch_Ilkin') Highest probability examples for relation has_sibling: 1.000 KBTriple(rel='has_sibling', sbj='Ishmael', obj='Abraham') 1.000 KBTriple(rel='has_sibling', sbj='Abraham', obj='Ishmael') 1.000 KBTriple(rel='has_sibling', sbj='Sergey_Brin', obj='Larry_Page') 1.000 KBTriple(rel='has_sibling', sbj='Larry_Page', obj='Sergey_Brin') 1.000 KBTriple(rel='has_sibling', sbj='Isaac', obj='Abraham') 1.000 KBTriple(rel='has_sibling', sbj='Abraham', obj='Isaac') 1.000 KBTriple(rel='has_sibling', sbj='Jamie_Lee_Curtis', obj='Janet_Leigh') 1.000 KBTriple(rel='has_sibling', sbj='Janet_Leigh', obj='Jamie_Lee_Curtis') 1.000 KBTriple(rel='has_sibling', sbj='Karl_Marx', obj='Friedrich_Engels') 1.000 KBTriple(rel='has_sibling', sbj='Friedrich_Engels', obj='Karl_Marx') Highest probability examples for relation has_spouse: 1.000 KBTriple(rel='has_spouse', sbj='Sergey_Brin', obj='Larry_Page') 1.000 KBTriple(rel='has_spouse', sbj='Larry_Page', obj='Sergey_Brin') 1.000 KBTriple(rel='has_spouse', sbj='Isidor_Straus', obj='Denver') 1.000 KBTriple(rel='has_spouse', sbj='Denver', obj='Isidor_Straus') 1.000 KBTriple(rel='has_spouse', sbj='Karl_Marx', obj='Friedrich_Engels') 1.000 KBTriple(rel='has_spouse', sbj='Friedrich_Engels', obj='Karl_Marx') 1.000 KBTriple(rel='has_spouse', sbj='Rajiv_Gandhi', obj='Indira_Gandhi') 1.000 KBTriple(rel='has_spouse', sbj='Indira_Gandhi', obj='Rajiv_Gandhi') 0.999 KBTriple(rel='has_spouse', sbj='Anne_Boleyn', obj='Elizabeth_I_of_England') 0.999 KBTriple(rel='has_spouse', sbj='Elizabeth_I_of_England', obj='Anne_Boleyn') Highest probability examples for relation is_a: 1.000 KBTriple(rel='is_a', sbj='Insect', obj='Lepidoptera') 1.000 KBTriple(rel='is_a', sbj='Lepidoptera', obj='Insect') 1.000 KBTriple(rel='is_a', sbj='Apidae', obj='Bee') 1.000 KBTriple(rel='is_a', sbj='Bee', obj='Apidae') 1.000 KBTriple(rel='is_a', sbj='Odonata', obj='Insect') 1.000 KBTriple(rel='is_a', sbj='Insect', obj='Odonata') 1.000 KBTriple(rel='is_a', sbj='Malvaceae', obj='Hibiscus') 1.000 KBTriple(rel='is_a', sbj='Hibiscus', obj='Malvaceae') 1.000 KBTriple(rel='is_a', sbj='Okra', obj='Malvaceae') 1.000 KBTriple(rel='is_a', sbj='Malvaceae', obj='Okra') Highest probability examples for relation nationality: 1.000 KBTriple(rel='nationality', sbj='Sri_Lanka', obj='Matale_District') 1.000 KBTriple(rel='nationality', sbj='Matale_District', obj='Sri_Lanka') 1.000 KBTriple(rel='nationality', sbj='North_Island', obj='New_Zealand') 1.000 KBTriple(rel='nationality', sbj='New_Zealand', obj='North_Island') 1.000 KBTriple(rel='nationality', sbj='Systema_Naturae', obj='Carl_Linnaeus') 1.000 KBTriple(rel='nationality', sbj='Carl_Linnaeus', obj='Systema_Naturae') 1.000 KBTriple(rel='nationality', sbj='Insect', obj='Lepidoptera') 1.000 KBTriple(rel='nationality', sbj='Lepidoptera', obj='Insect') 1.000 KBTriple(rel='nationality', sbj='California', obj='San_Francisco_Bay_Area') 1.000 KBTriple(rel='nationality', sbj='San_Francisco_Bay_Area', obj='California') Highest probability examples for relation parents: 1.000 KBTriple(rel='parents', sbj='Ishmael', obj='Abraham') 1.000 KBTriple(rel='parents', sbj='Isaac', obj='Abraham') 1.000 KBTriple(rel='parents', sbj='Kim_Jong-il', obj='Kim_Jong-un') 1.000 KBTriple(rel='parents', sbj='Kim_Jong-un', obj='Kim_Jong-il') 1.000 KBTriple(rel='parents', sbj='Abraham', obj='Isaac') 1.000 KBTriple(rel='parents', sbj='Abraham', obj='Ishmael') 1.000 KBTriple(rel='parents', sbj='Anne_Boleyn', obj='Elizabeth_I_of_England') 1.000 KBTriple(rel='parents', sbj='Elizabeth_I_of_England', obj='Anne_Boleyn') 1.000 KBTriple(rel='parents', sbj='Louis_the_Pious', obj='Charlemagne') 1.000 KBTriple(rel='parents', sbj='Charlemagne', obj='Louis_the_Pious') Highest probability examples for relation place_of_birth: 1.000 KBTriple(rel='place_of_birth', sbj='Sri_Lanka', obj='Matale_District') 1.000 KBTriple(rel='place_of_birth', sbj='Matale_District', obj='Sri_Lanka') 1.000 KBTriple(rel='place_of_birth', sbj='North_Island', obj='New_Zealand') 1.000 KBTriple(rel='place_of_birth', sbj='New_Zealand', obj='North_Island') 1.000 KBTriple(rel='place_of_birth', sbj='Illinois', obj='United_States_Senate') 1.000 KBTriple(rel='place_of_birth', sbj='United_States_Senate', obj='Illinois') 0.998 KBTriple(rel='place_of_birth', sbj='California', obj='San_Francisco_Bay_Area') 0.998 KBTriple(rel='place_of_birth', sbj='San_Francisco_Bay_Area', obj='California') 0.988 KBTriple(rel='place_of_birth', sbj='Pangasinan', obj='Philippines') 0.988 KBTriple(rel='place_of_birth', sbj='Philippines', obj='Pangasinan') Highest probability examples for relation place_of_death: 1.000 KBTriple(rel='place_of_death', sbj='Systema_Naturae', obj='Carl_Linnaeus') 1.000 KBTriple(rel='place_of_death', sbj='Carl_Linnaeus', obj='Systema_Naturae') 1.000 KBTriple(rel='place_of_death', sbj='Ishmael', obj='Abraham') 1.000 KBTriple(rel='place_of_death', sbj='Abraham', obj='Ishmael') 1.000 KBTriple(rel='place_of_death', sbj='Sri_Lanka', obj='Matale_District') 1.000 KBTriple(rel='place_of_death', sbj='Matale_District', obj='Sri_Lanka') 1.000 KBTriple(rel='place_of_death', sbj='North_Island', obj='New_Zealand') 1.000 KBTriple(rel='place_of_death', sbj='New_Zealand', obj='North_Island') 0.999 KBTriple(rel='place_of_death', sbj='Chernobyl_Nuclear_Power_Plant', obj='Ukraine') 0.999 KBTriple(rel='place_of_death', sbj='Ukraine', obj='Chernobyl_Nuclear_Power_Plant') Highest probability examples for relation profession: 1.000 KBTriple(rel='profession', sbj='Eyeless_in_Gaza', obj='Aldous_Huxley') 1.000 KBTriple(rel='profession', sbj='Aldous_Huxley', obj='Eyeless_in_Gaza') 0.999 KBTriple(rel='profession', sbj='Hispania', obj='Spain') 0.999 KBTriple(rel='profession', sbj='Spain', obj='Hispania') 0.996 KBTriple(rel='profession', sbj='Screenwriter', obj='Actor') 0.996 KBTriple(rel='profession', sbj='Actor', obj='Screenwriter') 0.995 KBTriple(rel='profession', sbj='Tunch_Ilkin', obj='Tunch_Ilkin') 0.995 KBTriple(rel='profession', sbj='Blog_award', obj='Blog_award') 0.995 KBTriple(rel='profession', sbj='Physicist', obj='Nikola_Tesla') 0.995 KBTriple(rel='profession', sbj='Guitarist', obj='Robby_Krieger') Highest probability examples for relation worked_at: 1.000 KBTriple(rel='worked_at', sbj='Sri_Lanka', obj='Matale_District') 1.000 KBTriple(rel='worked_at', sbj='Matale_District', obj='Sri_Lanka') 1.000 KBTriple(rel='worked_at', sbj='North_Island', obj='New_Zealand') 1.000 KBTriple(rel='worked_at', sbj='New_Zealand', obj='North_Island') 1.000 KBTriple(rel='worked_at', sbj='Insect', obj='Lepidoptera') 1.000 KBTriple(rel='worked_at', sbj='Lepidoptera', obj='Insect') 1.000 KBTriple(rel='worked_at', sbj='Austria', obj='Gaston_Glock') 1.000 KBTriple(rel='worked_at', sbj='Gaston_Glock', obj='Austria') 1.000 KBTriple(rel='worked_at', sbj='L._Ron_Hubbard', obj='Church_of_Scientology') 1.000 KBTriple(rel='worked_at', sbj='Church_of_Scientology', obj='L._Ron_Hubbard')
There are actually some good discoveries here! The predictions for the author
relation seem especially good. Of course, there are also plenty of bad results, and a few that are downright comical. We may hope that as we improve our models and optimize performance in our automatic evaluations, the results we observe in this manual evaluation improve as well.
Exercise: Note that every time we predict that a given relation holds between entities X
and Y
, we also predict, with equal confidence, that it holds between Y
and X
. Why? How could we fix this?
[ top ]
Our current model is quite rudimentary — it's merely a starting point for further exploration. This section lists a number of suggestions for next steps. Pursue whatever ideas look most promising! Your immediate goal is to optimize macro-averaged F0.5-score — but don't blinker yourself. Consider other ways of evaluating performance, and remember that the ultimate goal is to extract new relational triples.
LogisticRegression
model in sklearn
does L2 regularization by default. Try using L1 regularization instead. sklearn
makes this very easy: it provides implementations for everything from elastic nets to SVMs to gradient boosting.[ top ]
The purpose of this homework is to begin exploring larger, more diverse feature space, to figure out which ones lead to improved classifiers.
As a reminder, our baseline classifier (using simple_bag_of_words_featurizer
looks like this) when run on the dev
set with the standard model_factory
.
baseline = experiment(featurizers=[simple_bag_of_words_featurizer])
Building datasets Featurizing Training models Training complete relation precision recall f-score support size ------------------ --------- --------- --------- --------- --------- adjoins 0.874 0.459 0.740 303 5319 author 0.830 0.558 0.756 480 5496 capital 0.677 0.236 0.493 89 5105 contains 0.752 0.615 0.720 2667 7683 film_performance 0.820 0.580 0.757 822 5838 founders 0.837 0.387 0.679 359 5375 genre 0.619 0.235 0.467 166 5182 has_sibling 0.870 0.234 0.563 513 5529 has_spouse 0.900 0.362 0.694 575 5591 is_a 0.710 0.223 0.494 494 5510 nationality 0.584 0.145 0.363 311 5327 parents 0.877 0.569 0.791 325 5341 place_of_birth 0.759 0.203 0.490 217 5233 place_of_death 0.621 0.124 0.345 145 5161 profession 0.639 0.159 0.399 245 5261 worked_at 0.740 0.250 0.532 228 5244 ------------------ --------- --------- --------- --------- --------- macro-average 0.757 0.334 0.580 7939 88195
The current bag-of-words representation makes no distinction between "forward" and "reverse" examples. But, intuitively, there is big difference between X and his son Y and Y and his son X. This question asks you to modify simple_bag_of_words_featurizer
to capture these differences.
To submit:
A feature function directional_bag_of_words_featurizer
that is just like simple_bag_of_words_featurizer
except that it distinguishes "forward" and "reverse". To do this, you just need to mark each word feature for whether it is derived from a subject–object example or from an object–subject example. The precise nature of the mark you add for the two cases doesn't make a difference to the model.
The macro-average F-score on the dev
set that you obtain from running experiment
with directional_bag_of_words_featurizer
as the only featurizer. (Aside from this, use all the default values for experiment
.)
experiment
returns some of the core objects used in the experiment. How many feature names does the vectorizer
have for the experiment run in the previous step? (Note: we're partly asking you to figure out how to get this value by using the sklearn documentation, so please don't ask how to do it on Piazza!)
Our corpus distribution contains part-of-speech (POS) tagged versions of the core text spans. Let's begin to explore whether there is information in these sequences, focusing on middle_POS
.
To submit:
middle_bigram_pos_tag_featurizer
that is just like simple_bag_of_words_featurizer
except that it creates a feature for bigram POS sequences. For example, givenThe/DT dog/N napped/V
we obtain the list of bigram POS sequences
['<s> DT', 'DT N', 'N V', 'V </s>']
.
Don't forget the start and end tags, to model those environments properly!
dev
set that you obtain from running experiment
with middle_bigram_pos_tag_featurizer
as the only featurizer. (Aside from this, use all the default values for experiment
.)Note: To parse middle_POS
, one splits on whitespace to get the word/TAG
pairs. Each of these pairs s
can be parsed with s.rsplit('/', 1)
.
The following allows you to use NLTK's WordNet API to get the synsets compatible with dog as used as a noun:
from nltk.corpus import wordnet as wn
dog = wn.synsets('dog', pos='n')
This question asks you to create synset-based features from the word/tag pairs in middle_POS
.
To convert the tags in the corpus to WordNet tags:
Tag begins with | WordNet pos value |
---|---|
N |
'n' |
V |
'v' |
J |
'a' |
R |
'r' |
Otherwise | None |
To submit:
A feature function synset_featurizer
that is just like simple_bag_of_words_featurizer
except that it creates a count dictionary where the keys are synsets, as derived from the unigrams in the middle_POS
field using wn.synsets
. Stringify the synsets with str
so that they can be dict
keys. Use the table above to convert tags to pos
arguments usable by wn.synsets
.
The macro-average F-score on the dev
set that you obtain from running experiment
with synset_featurizer
as the only featurizer. (Aside from this, use all the default values for experiment
.)
Run experiment
with all directional_bag_of_words_featurizer
, middle_bigram_pos_tag_featurizer
, and synset_featurizer
together as the featurizers. Let's see if all this work paid off in terms of raw peformance!
To submit:
The macro-average F-score on the dev
set that you obtain from running this experiment. (Aside from featurizers
, use all the default values for experiment
.)
The number of feature names contained in the vectorizer
for this experiment.
Note: You'll see that this is a very large model. We want you to submit with the default value for model_factory
, but it is worth trying variants that are more suitable for these inputs – penalty="l1"
for LogisticRegression
is a good starting point, as it will give 0 weight to many uninformative features.
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The goal of the bake-off for this unit is very simple: to achieve the best macro-averaged F0.5-score on the test
split. The Next steps section suggested a number of possible strategies for improving the baseline model, and Homework 3 explores several more. But of course these are by no means exhaustive. You can surely come up with lots of additional ideas. The sky is the limit!
There's only one strict rule here: you must not evaluate on the test
split until you ready to report your bake-off results. All evaluations during development must be on the dev
split. To do otherwise would be methodologically unsound and intellectually dishonest!
Your bake-off submission should include:
test
split.Submission URL: https://goo.gl/forms/ohqzpnHMwHIT7f642
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