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python

Chunk Extraction with NLTK

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Chunk extraction is a useful preliminary step to information extraction, that creates parse trees from unstructured text with a chunker. Once you have a parse tree of a sentence, you can do more specific information extraction, such as named entity recognition and relation extraction.

Chunking is basically a 3 step process:

  1. Tag a sentence
  2. Chunk the tagged sentence
  3. Analyze the parse tree to extract information

I’ve already written about how to train a NLTK part of speech tagger and a chunker, so I’ll assume you’ve already done the training, and now you want to use your pos tagger and iob chunker to do something useful.

IOB Tag Chunker

The previously trained chunker is actually a chunk tagger. It’s a Tagger that assigns IOB chunk tags to part-of-speech tags. In order to use it for proper chunking, we need some extra code to convert the IOB chunk tags into a parse tree. I’ve created a wrapper class that complies with the nltk ChunkParserI interface and uses the trained chunk tagger to get IOB tags and convert them to a proper parse tree.

import nltk.chunk
import itertools

class TagChunker(nltk.chunk.ChunkParserI):
    def __init__(self, chunk_tagger):
        self._chunk_tagger = chunk_tagger

    def parse(self, tokens):
        # split words and part of speech tags
        (words, tags) = zip(*tokens)
        # get IOB chunk tags
        chunks = self._chunk_tagger.tag(tags)
        # join words with chunk tags
        wtc = itertools.izip(words, chunks)
        # w = word, t = part-of-speech tag, c = chunk tag
        lines = [' '.join([w, t, c]) for (w, (t, c)) in wtc if c]
        # create tree from conll formatted chunk lines
        return nltk.chunk.conllstr2tree('\n'.join(lines))

Chunk Extraction

Now that we have a proper NLTK chunker, we can use it to extract chunks. Here’s a simple example that tags a sentence, chunks the tagged sentence, then prints out each noun phrase.

# sentence should be a list of words
tagged = tagger.tag(sentence)
tree = chunker.parse(tagged)
# for each noun phrase sub tree in the parse tree
for subtree in tree.subtrees(filter=lambda t: t.node == 'NP'):
    # print the noun phrase as a list of part-of-speech tagged words
    print subtree.leaves()

Each sub tree has a phrase tag, and the leaves of a sub tree are the tagged words that make up that chunk. Since we’re training the chunker on IOB tags, NP stands for Noun Phrase. As noted before, the results of this natural language processing are heavily dependent on the training data. If your input text isn’t similar to the your training data, then you probably won’t be getting many chunks.

python

How to Train a NLTK Chunker

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In NLTK, chunking is the process of extracting short, well-formed phrases, or chunks, from a sentence. This is also known as partial parsing, since a chunker is not required to capture all the words in a sentence, and does not produce a deep parse tree. But this is a good thing because it’s very hard to create a complete parse grammar for natural language, and full parsing is usually all or nothing. So chunking allows you to get at the bits you want and ignore the rest.

Training a Chunker

The general approach to chunking and parsing is to define rules or expressions that are then matched against the input sentence. But this is a very manual, tedious, and error-prone process, likely to get very complicated real fast. The alternative approach is to train a chunker the same way you train a part-of-speech tagger. Except in this case, instead of training on (word, tag) sequences, we train on (tag, iob) sequences, where iob is a chunk tag defined in the the conll2000 corpus. Here’s a function that will take a list of chunked sentences (from a chunked corpus like conll2000 or treebank), and return a list of (tag, iob) sequences.

import nltk.chunk

def conll_tag_chunks(chunk_sents):
    tag_sents = [nltk.chunk.tree2conlltags(tree) for tree in chunk_sents]
    return [[(t, c) for (w, t, c) in chunk_tags] for chunk_tags in tag_sents]

Chunker Accuracy

So how accurate is the trained chunker? Here’s the rest of the code, followed by a chart of the accuracy results. Note that I’m only using Ngram Taggers. You could additionally use the BrillTagger, but the training takes a ridiculously long time for very minimal gains in accuracy.

import nltk.corpus, nltk.tag

def ubt_conll_chunk_accuracy(train_sents, test_sents):
    train_chunks = conll_tag_chunks(train_sents)
    test_chunks = conll_tag_chunks(test_sents)

    u_chunker = nltk.tag.UnigramTagger(train_chunks)
    print 'u:', nltk.tag.accuracy(u_chunker, test_chunks)

    ub_chunker = nltk.tag.BigramTagger(train_chunks, backoff=u_chunker)
    print 'ub:', nltk.tag.accuracy(ub_chunker, test_chunks)

    ubt_chunker = nltk.tag.TrigramTagger(train_chunks, backoff=ub_chunker)
    print 'ubt:', nltk.tag.accuracy(ubt_chunker, test_chunks)

    ut_chunker = nltk.tag.TrigramTagger(train_chunks, backoff=u_chunker)
    print 'ut:', nltk.tag.accuracy(ut_chunker, test_chunks)

    utb_chunker = nltk.tag.BigramTagger(train_chunks, backoff=ut_chunker)
    print 'utb:', nltk.tag.accuracy(utb_chunker, test_chunks)

# conll chunking accuracy test
conll_train = nltk.corpus.conll2000.chunked_sents('train.txt')
conll_test = nltk.corpus.conll2000.chunked_sents('test.txt')
ubt_conll_chunk_accuracy(conll_train, conll_test)

# treebank chunking accuracy test
treebank_sents = nltk.corpus.treebank_chunk.chunked_sents()
ubt_conll_chunk_accuracy(treebank_sents[:2000], treebank_sents[2000:])
Accuracy for Trained Chunker
Accuracy for Trained Chunker

The ub_chunker and utb_chunker are slight favorites with equal accuracy, so in practice I suggest using the ub_chunker since it takes slightly less time to train.

Conclusion

Training a chunker this way is much easier than creating manual chunk expressions or rules, it can approach 100% accuracy, and the process is re-usable across data sets. As with part-of-speech tagging, the training set really matters, and should be as similar as possible to the actual text that you want to tag and chunk.

python

Part of Speech Tagging with NLTK Part 3 – Brill Tagger

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In regexp and affix pos tagging, I showed how to produce a Python NLTK part-of-speech tagger using Ngram pos tagging in combination with Affix and Regex pos tagging, with accuracy approaching 90%. In part 3, I’ll use the brill tagger to get the accuracy up to and over 90%.

NLTK Brill Tagger

The BrillTagger is different than the previous part of speech taggers. For one, it’s not a SequentialBackoffTagger, though it does use an initial pos tagger, which in our case will be the raubt_tagger from part 2. The brill tagger uses the initial pos tagger to produce initial part of speech tags, then corrects those pos tags based on brill transformational rules. These rules are learned by training the brill tagger with the FastBrillTaggerTrainer and rules templates. Here’s an example, with templates copied from the demo() function in nltk.tag.brill.py. Refer to ngram part of speech tagging for the backoff_tagger function and the train_sents, and regexp part of speech tagging for the word_patterns.

import nltk.tag
from nltk.tag import brill

raubt_tagger = backoff_tagger(train_sents, [nltk.tag.AffixTagger,
    nltk.tag.UnigramTagger, nltk.tag.BigramTagger, nltk.tag.TrigramTagger],
    backoff=nltk.tag.RegexpTagger(word_patterns))

templates = [
    brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule, (1,1)),
    brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule, (2,2)),
    brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule, (1,2)),
    brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule, (1,3)),
    brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule, (1,1)),
    brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule, (2,2)),
    brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule, (1,2)),
    brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule, (1,3)),
    brill.ProximateTokensTemplate(brill.ProximateTagsRule, (-1, -1), (1,1)),
    brill.ProximateTokensTemplate(brill.ProximateWordsRule, (-1, -1), (1,1))
]

trainer = brill.FastBrillTaggerTrainer(raubt_tagger, templates)
braubt_tagger = trainer.train(train_sents, max_rules=100, min_score=3)

NLTK Brill Tagger Accuracy

So now we have a braubt_tagger. You can tweak the max_rules and min_score params, but be careful, as increasing the values will exponentially increase the training time without significantly increasing accuracy. In fact, I found that increasing the min_score tended to decrease the accuracy by a percent or 2. So here’s how the braubt_tagger fares against the other NLTK part of speech taggers.

Conclusion

There’s certainly more you can do for part-of-speech tagging with nltk & python, but the brill tagger based braubt_tagger should be good enough for many purposes. The most important component of part-of-speech tagging is using the correct training data. If you want your pos tagger to be accurate, you need to train it on a corpus similar to the text you’ll be tagging. The brown, conll2000, and treebank corpora are what they are, and you shouldn’t assume that a pos tagger trained on them will be accurate on a different corpus. For example, a pos tagger trained on one part of the brown corpus may be 90% accurate on other parts of the brown corpus, but only 50% accurate on the conll2000 corpus. But a pos tagger trained on the conll2000 corpus will be accurate for the treebank corpus, and vice versa, because conll2000 and treebank are quite similar. So make sure you choose your training data carefully.

If you’d like to try to push NLTK part of speech tagging accuracy even higher, see part 4, where I compare the brill tagger to classifier based pos taggers, and nltk.tag.pos_tag.