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Corpus Bootstrapping with NLTK
by Jacob Perkins
Jacob Perkins


 http://www.weotta.com
 http://streamhacker.com
 http://text-processing.com
 https://github.com/japerk/nltk-trainer
 @japerk
Problem



 you want to do NLProc
 many proven supervised training algorithms
 but you don’t have a training corpus
Solution




 make a custom training corpus
Problems with Manual Annotation



 takes time
 requires expertise
 expert time costs $$$
Solution: Bootstrap


 less time
 less expertise
 costs less
 requires thinking & creativity
Corpus Bootstrapping at Weotta



 review sentiment
 keyword classification
 phrase extraction & classification
Bootstrapping Examples



 english -> spanish sentiment
 phrase extraction
Translating Sentiment



 start with english sentiment corpus & classifier
 english -> spanish -> spanish
English -> Spanish -> Spanish

1. translate english examples to spanish
2. train classifier
3. classify spanish text into new corpus
4. correct new corpus
5. retrain classifier
6. add to corpus & goto 4 until done
Translate Corpus


$ translate_corpus.py movie_reviews --source english
--target spanish
Train Initial Classifier



$ train_classifier.py spanish_movie_reviews
Create New Corpus


$ classify_to_corpus.py spanish_sentiment --input
spanish_examples.txt --classifier
spanish_movie_reviews_NaiveBayes.pickle
Manual Correction



1. scan each file
2. move incorrect examples to correct file
Train New Classifier



$ train_classifier.py spanish_sentiment
Adding to the Corpus

 start with >90% probability
 retrain
 carefully decrease probability threshold
Add more at a Lower Threshold


$ classify_to_corpus.py categorized_corpus --
classifier categorized_corpus_NaiveBayes.pickle --
threshold 0.8 --input new_examples.txt
When are you done?



 what level of accuracy do you need?
 does your corpus reflect real text?
 how much time do you have?
Tips


 garbage in, garbage out
 correct bad data
 clean & scrub text
 experiment with train_classifier.py options
 create custom features
Bootstrapping a Phrase Extractor
1. find a pos tagged corpus
2. annotate raw text
3. train pos tagger
4. create pos tagged & chunked corpus
5. tag unknown words
6. train pos tagger & chunker
7. correct errors
8. add to corpus, goto 5 until done
NLTK Tagged Corpora

 English: brown, conll2000, treebank
 Portuguese: mac_morpho, floresta
 Spanish: cess_esp, conll2002
 Catalan: cess_cat
 Dutch: alpino, conll2002
 Indian Languages: indian
 Chinese: sinica_treebank
 see http://text-processing.com/demo/tag/
Train Tagger



$ train_tagger.py treebank --simplify_tags
Phrase Annotation


Hello world, [this is an important phrase].
Tag Phrases


$ tag_phrases.py my_corpus --tagger
treebank_simplify_tags.pickle --input my_phrases.txt
Chunked & Tagged Phrase


Hello/N world/N ,/, [ this/DET is/V an/DET
important/ADJ phrase/N ] ./.
Correct Unknown Words



1. find -NONE- tagged words
2. fix tags
Train New Tagger


$ train_tagger.py my_corpus --reader
nltk.corpus.reader.ChunkedCorpusReader
Train Chunker


$ train_chunker.py my_corpus --reader
nltk.corpus.reader.ChunkedCorpusReader
Extracting Phrases
import collections, nltk.data
from nltk import tokenize
from nltk.tag import untag

tagger = nltk.data.load('taggers/my_corpus_tagger.pickle')
chunker = nltk.data.load('chunkers/my_corpus_chunker.pickle')

def extract_phrases(t):
    d = collections.defaultdict(list)
    for sub in t.subtrees(lambda s: s.node != 'S'):
        d[sub.node].append(' '.join(untag(sub.leaves())))
    return d

sents = tokenize.sent_tokenize(text)
words = tokenize.word_tokenize(sents[0])
d = extract_phrases(chunker.parse(tagger.tag(words)))
# defaultdict(<type 'list'>, {'PHRASE_TAG': ['phrase']})
Final Tips


 error correction is faster than manual annotation
 find close enough corpora
 use nltk-trainer to experiment
 iterate -> quality
 no substitute for human judgement
Links



http://www.nltk.org
https://github.com/japerk/nltk-trainer
http://text-processing.com

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