NLTK: the Good, the Bad, and the Awesome
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Presented at the first meeting of the Bay Area NLP group: http://www.meetup.com/Bay-Area-NLP/events/16522295/ by Jacob Perkins.
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NLTK: the Good, the Bad, and the Awesome
- 1. NLTK The Good, the Bad, and the Awesome
- 2. Jacob Perkins • Python Text Processing with NLTK 2.0 Cookbook • streamhacker.com • weotta.com • text-processing.com • @japerk
- 3. The Good • Makes NLProc easier and more accessible • Python (great learning language) • Lots of documentation (and 2 books!) • Designed for training custom models • Includes many training corpora • Many algorithms to experiment with
- 4. The Bad • NLProc is hard • Few out-of-the-box solutions (see Pattern) • Not designed for big-data (see Mahout) • Doesn’t have latest algorithms (see Scikits-Learn) • No online or active learning algorithms
- 5. More Bad • Doesn’t play nice with pip or easy_install • Python (Java: StanfordNLP, OpenNLP, Gate, Mahout) • Models can use a lot of memory (& disk if pickled)
- 6. The Awesome • Great for education and research • Lots of users & active community • Extensible interfaces • Training algorithms span human languages
- 7. More Awesome • Trained models can be very fast • Well known algorithms can be very accurate • NLTK-Trainer (train models with 0 code) • Corpus bootstrapping
- 8. Some Numbers • 3 Classification Algorithms • 9 Part-of-Speech Tagging Algorithms • Stemming Algorithms for 15 Languages • 5 Word Tokenization Algorithms • Sentence Tokenizers for 16 Languages • 60 included corpora
- 9. Text-Processing.com • NLTK Demos & APIs • Sentiment Analysis • Part-of-Speech Tagging & Chunking / NER • Stemming • Tokenization
- 12. NLTK-Trainer • https://github.com/japerk/nltk-trainer • 3 Training Command Scripts ‣ train_classifier.py ‣ train_tagger.py ‣ train_chunker.py • Easy to tweak training parameters • Duck-Typed corpus reading
- 13. Training Classifiers • train_classifier.py movie_reviews --instances paras • train_classifier.py movie_reviews --instances paras --min_score 2 --ngrams 1 --ngrams 2 • train_classifier.py movie_reviews --instances paras --classifier MEGAM • train_classifier.py movie_reviews --instances paras --cross-fold 10 • Pickled models are saved in ~/nltk_data/classifiers/
- 14. Training Taggers • train_tagger.py treebank • train_tagger.py treebank --sequential ubt -- brill • train_tagger.py treebank --sequential ‘’ -- classifier NaiveBayes • train_tagger.py mac_morpho --simplify_tags • Pickled models are saved in ~/nltk_data/taggers/
- 15. Training Chunkers • train_chunker.py treebank_chunk • train_chunker.py treebank_chunk --classifier NaiveBayes • train_chunker.py conll2000 --fileids train.txt • Pickled models are saved in ~/nltk_data/chunkers/
- 16. Corpus Bootstrapping • Guess & Correct easier than starting from scratch • Use an existing model for initial guesses • emoticons ‣ :) = “pos” ‣ :( = “neg” • ratings ‣ 5 stars = “pos” ‣ 1 star = “neg”
- 17. Portuguese Phrase Extraction & Classification • similar to condensr.com • Brazilian Portuguese • aspect classification is easy with training corpus • need chunked corpus for phrase extraction • use mac_morpho & nltk-trainer to train initial tagger • part-of-speech tag annotation is time consuming • simplified tags are much easier • bracketed phrases w/out pos tags
- 18. treebank_chunk [ Pierre/NNP Vinken/NNP ] ,/, [ 61/CD years/NNS ] old/JJ ,/, will/MD join/VB [ the/DT board/NN ] as/IN [ a/DT nonexecutive/JJ director/NN Nov./ NNP 29/CD ] ./.
- 19. Just Brackets [ Pierre Vinken ] , [ 61 years ] old , will join [ the board ] as [ a nonexecutive director Nov. 29 ] .
- 20. NLP at Weotta • Parsing & information extraction • Text cleaning & normalization (more parsing) • Text & keyword classification • De-duplication • Search indexing / IR • Sentiment analysis • Human integration