Natural Language Processing and Python

5,420 views

Published on

A short presentation on basic NLP concepts and computational challenges using Python tools such as NLTK & Gensim.

Presented by Ann C. Tan-Pohlmann

Published in: Technology

Natural Language Processing and Python

  1. 1. Natural Language Processing + Python by Ann C. Tan-Pohlmann February 22, 2014
  2. 2. Outline • NLP Basics • NLTK – Text Processing • Gensim (really, really short ) – Text Classification 2
  3. 3. Natural Language Processing • computer science, artificial intelligence, and linguistics • human–computer interaction • natural language understanding • natural language generation - Wikipedia 3
  4. 4. Star Trek's Universal Translator http://www.youtube.com/watch?v=EaeSKU V2zp0
  5. 5. Spoken Dialog Systems 5
  6. 6. NLP Basics • Morphology – study of word formation – how word forms vary in a sentence • Syntax – branch of grammar – how words are arranged in a sentence to show connections of meaning • Semantics – study of meaning of words, phrases and sentences 6
  7. 7. NLTK: Getting Started • Natural Language Took Kit – for symbolic and statistical NLP – teaching tool, study tool and as a platform for prototyping • Python 2.7 is a prerequisite >>> import nltk >>> nltk.download() 7
  8. 8. Some NLTK methods • • • • • Frequency Distribution text.similar(str) concordance(str) len(text) len(set(text)) lexical_diversity • • • • • – len(text)/ len(set(text)) fd = FreqDist(text) fd.inc(str) fd[str] fd.N() fd.max() • text.collocations() - sequence of words that occur together often MORPHOLOGY > Syntax > Semantics 8
  9. 9. Frequency Distribution • • • • • fd = FreqDist(text) fd.inc(str) – increment count fd[str] – returns the number of occurrence for sample str fd.N() – total number of samples fd.max() – sample with the greatest count 9
  10. 10. Corpus • large collection of raw or categorized text on one or more domain • Examples: Gutenberg, Brown, Reuters, Web & Chat Txt >>> from nltk.corpus import brown >>> brown.categories() ['adventure', 'belles_lettres', 'editorial', 'fiction', 'government', 'hobbies', ' humor', 'learned', 'lore', 'mystery', 'news', 'religion', 'reviews', 'romance', 'science_fiction'] >>> adventure_text = brown.words(categories='adventure') 10
  11. 11. Corpora in Other Languages >>> from nltk.corpus import udhr >>> languages = nltk.corpus.udhr.fileids() >>> languages.index('Filipino_Tagalog-Latin1') >>> tagalog = nltk.corpus.udhr.raw('Filipino_Tagalog-Latin1') >>> tagalog_words = nltk.corpus.udhr.words('Filipino_Tagalog-Latin1') >>> tagalog_tokens = nltk.word_tokenize(tagalog) >>> tagalog_text = nltk.Text(tagalog_tokens) >>> fd = FreqDist(tagalog_text) >>> for sample in fd: ... print sample 11
  12. 12. Using Corpus from Palito Corpus – large collection of raw or categorized text >>> import nltk >>> from nltk.corpus import PlaintextCorpusReader >>> corpus_dir = '/Users/ann/Downloads' >>> tagalog = PlaintextCorpusReader(corpus_dir, 'Tagalog_Literary_Text.txt') >>> raw = tagalog.raw() >>> sentences = tagalog.sents() >>> words = tagalog.words() >>> tokens = nltk.word_tokenize(raw) >>> tagalog_text = nltk.Text(tokens) 12
  13. 13. Spoken Dialog Systems MORPHOLOGY > Syntax > Semantics 13
  14. 14. Tokenization Tokenization – breaking up of string into words and punctuations >>> tokens = nltk.word_tokenize(raw) >>> tagalog_tokens = nltk.Text(tokens) >>> tagalog_tokens = set(sample.lower() for sample in tagalog_tokens) MORPHOLOGY > Syntax > Semantics 14
  15. 15. Stemming Stemming – normalize words into its base form, result may not be the 'root' word >>> def stem(word): ... for suffix in ['ing', 'ly', 'ed', 'ious', 'ies', 'ive', 'es', 's', 'ment']: ... if word.endswith(suffix): ... return word[:-len(suffix)] ... return word ... >>> stem('reading') 'read' >>> stem('moment') 'mo' MORPHOLOGY > Syntax > Semantics 15
  16. 16. Lemmatization Lemmatization – uses vocabulary list and morphological analysis (uses POS of a word) >>> def stem(word): ... for suffix in ['ing', 'ly', 'ed', 'ious', 'ies', 'ive', 'es', 's', 'ment']: ... if word.endswith(suffix) and word[:-len(suffix)] in brown.words(): ... return word[:-len(suffix)] ... return word ... >>> stem('reading') 'read' >>> stem('moment') 'moment' MORPHOLOGY > Syntax > Semantics 16
  17. 17. NLTK Stemmers & Lemmatizer • Porter Stemmer and Lancaster Stemmer >>> porter = nltk.PorterStemmer() >>> lancaster = nltk.LancasterStemmer() >>> [porter.stem(w) for w in brown.words()[:100]] • Word Net Lemmatizer >>> wnl = nltk.WordNetLemmatizer() >>> [wnl.lemmatize(w) for w in brown.words()[:100]] • Comparison >>> [wnl.lemmatize(w) for w in ['investigation', 'women']] >>> [porter.stem(w) for w in ['investigation', 'women']] >>> [lancaster.stem(w) for w in ['investigation', 'women']] MORPHOLOGY > Syntax > Semantics 17
  18. 18. Using Regular Expression Operator . ^abc abc$ [abc] [A-Z0-9] ed|ing|s * + ? {n} {n,} {,n} {m,n} a(b|c)+ Behavior Wildcard, matches any character Matches some pattern abc at the start of a string Matches some pattern abc at the end of a string Matches one of a set of characters Matches one of a range of characters Matches one of the specified strings (disjunction) Zero or more of previous item, e.g. a*, [a-z]* (also known as Kleene Closure) One or more of previous item, e.g. a+, [a-z]+ Zero or one of the previous item (i.e. optional), e.g. a?, [a-z]? Exactly n repeats where n is a non-negative integer At least n repeats No more than n repeats At least m and no more than n repeats Parentheses that indicate the scope of the operators MORPHOLOGY > Syntax > Semantics 18
  19. 19. Using Regular Expression >>> import re >>> re.findall(r'^(.*?)(ing|ly|ed|ious|ies|ive|es|s|ment)?$', 'reading') [('read', 'ing')] >>> def stem(word): ... regexp = r'^(.*?)(ing|ly|ed|ious|ies|ive|es|s|ment)?$' ... stem, suffix = re.findall(regexp, word)[0] ... return stem ... >>> stem('reading') 'read' >>> stem('moment') 'moment' MORPHOLOGY > Syntax > Semantics 19
  20. 20. Spoken Dialog Systems Morphology > SYNTAX > Semantics 20
  21. 21. Lexical Resources • collection of words with association information (annotation) • Ex: stopwords – high-frequency words with little lexical content >>> from nltk.corpus import stopwords >>> stopwords.words('english') >>> stopwords.words('german') MORPHOLOGY > Syntax > Semantics 21
  22. 22. Part-of-Speech (POS) Tagging • the process of labeling and classifying words to a particular part of speech based on its definition and context Morphology > SYNTAX > Semantics 22
  23. 23. NLTKs POS Tag Sets* – 1/2 Tag ADJ ADV CNJ DET EX FW MOD N NP Meaning adjective adverb conjunction determiner existential foreign word modal verb noun proper noun Examples new, good, high, special, big, local really, already, still, early, now and, or, but, if, while, although the, a, some, most, every, no there, there's dolce, ersatz, esprit, quo, maitre will, can, would, may, must, should year, home, costs, time, education Alison, Africa, April, Washington *simplified Morphology > SYNTAX > Semantics 23
  24. 24. NLTKs POS Tag Sets* – 2/2 Tag NUM PRO P TO UH V VD VG VN WH Meaning number pronoun preposition the word to interjection verb past tense present participle past participle wh determiner Examples twenty-four, fourth, 1991, 14:24 he, their, her, its, my, I, us on, of, at, with, by, into, under to ah, bang, ha, whee, hmpf, oops is, has, get, do, make, see, run said, took, told, made, asked making, going, playing, working given, taken, begun, sung who, which, when, what, where, how *simplified Morphology > SYNTAX > Semantics 24
  25. 25. NLTK POS Tagger (Brown) >>> nltk.pos_tag(brown.words()[:30]) [('The', 'DT'), ('Fulton', 'NNP'), ('County', 'NNP'), ('Grand', 'NNP'), ('Jury', 'NNP'), ('said', 'VBD'), ('Friday', 'NNP'), ('an', 'DT'), ('investigation', 'NN'), ('of', 'IN'), ("Atlanta's", 'JJ'), ('recent', 'JJ'), ('primary', 'JJ'), ('election', 'NN'), ('produced', 'VBN'), ('``', '``'), ('no', 'DT'), ('evidence', 'NN'), ("''", "''"), ('that', 'WDT'), ('any', 'DT'), ('irregularities', 'NNS'), ('took', 'VBD'), ('place', 'NN'), ('.', '.'), ('The', 'DT'), ('jury', 'NN'), ('further', 'RB'), ('said', 'VBD'), ('in', 'IN')] >>> brown.tagged_words(simplify_tags=True) [('The', 'DET'), ('Fulton', 'NP'), ('County', 'N'), ...] Morphology > SYNTAX > Semantics 25
  26. 26. NLTK POS Tagger (German) >>> german = nltk.corpus.europarl_raw.german >>> nltk.pos_tag(german.words()[:30]) [(u'Wiederaufnahme', 'NNP'), (u'der', 'NN'), (u'Sitzungsperiode', 'NNP'), (u'Ich', 'NNP'), (u'erklxe4re', 'NNP'), (u'die', 'VB'), (u'am', 'NN'), (u'Freita g', 'NNP'), (u',', ','), (u'dem', 'NN'), (u'17.', 'CD'), (u'Dezember', 'NNP'), (u' unterbrochene', 'NN'), (u'Sitzungsperiode', 'NNP'), (u'des', 'VBZ'), (u'Eur opxe4ischen', 'JJ'), (u'Parlaments', 'NNS'), (u'fxfcr', 'JJ'), (u'wiederaufg enommen', 'NNS'), (u',', ','), (u'wxfcnsche', 'NNP'), (u'Ihnen', 'NNP'), (u' nochmals', 'NNS'), (u'alles', 'VBZ'), (u'Gute', 'NNP'), (u'zum', 'NN'), (u'Ja hreswechsel', 'NNP'), (u'und', 'NN'), (u'hoffe', 'NN'), (u',', ',')] xe4 = ä xfc = ü !!! DOES NOT WORK FOR GERMAN Morphology > SYNTAX > Semantics 26
  27. 27. NLTK POS Dictionary >>> pos = nltk.defaultdict(lambda:'N') >>> pos['eat'] 'N' >>> pos.items() [('eat', 'N')] >>> for (word, tag) in brown.tagged_words(simplify_tags=True): ... if word in pos: ... if isinstance(pos[word], str): ... new_list = [pos[word]] ... pos[word] = new_list ... if tag not in pos[word]: ... pos[word].append(tag) ... else: ... pos[word] = [tag] ... >>> pos['eat'] ['N', 'V'] Morphology > SYNTAX > Semantics 27
  28. 28. What else can you do with NLTK? • Other Taggers – Unigram Tagging • nltk.UnigramTagger() • train tagger using tagged sentence data – N-gram Tagging • Text classification using machine learning techniques – decision trees – naïve Bayes classification (supervised) – Markov Models Morphology > SYNTAX > SEMANTICS 28
  29. 29. Gensim • Tool that extracts semantic structure of documents, by examining word statistical cooccurrence patterns within a corpus of training documents. • Algorithms: 1. Latent Semantic Analysis (LSA) 2. Latent Dirichlet Allocation (LDA) or Random Projections Morphology > Syntax > SEMANTICS 29
  30. 30. Gensim • Features – memory independent – wrappers/converters for several data formats • Vector – representation of the document as an array of features or question-answer pair 1. 2. 3. (word occurrence, count) (paragraph, count) (font, count) • Model – transformation from one vector to another – learned from a training corpus without supervision Morphology > Syntax > SEMANTICS 30
  31. 31. Wiki document classification http://radimrehurek.com/gensim/wiki.html 31
  32. 32. Other NLP tools for Python • TextBlob – part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation – https://pypi.python.org/pypi/textblob • Pattern – part-of-speech taggers, n-gram search, sentiment analysis, WordNet, machine learning – http://www.clips.ua.ac.be/pattern 32
  33. 33. Star Trek technology that became a reality http://www.youtube.com/watch?v=sRZxwR IH9RI
  34. 34. Installation Guides • NLTK – http://www.nltk.org/install.html – http://www.nltk.org/data.html • Gensim – http://radimrehurek.com/gensim/install.html • Palito – http://ccs.dlsu.edu.ph:8086/Palito/find_project.js p 34
  35. 35. Using iPython • http://ipython.org/install.html >>> documents = ["Human machine interface for lab abc computer applications", >>> "A survey of user opinion of computer system response time", >>> "The EPS user interface management system", >>> "System and human system engineering testing of EPS", >>> "Relation of user perceived response time to error measurement", >>> "The generation of random binary unordered trees", >>> "The intersection graph of paths in trees", >>> "Graph minors IV Widths of trees and well quasi ordering", >>> "Graph minors A survey"] 35
  36. 36. References • Natural Language Processing with Python By Steven Bird, Ewan Klein, Edward Loper • http://www.nltk.org/book/ • http://radimrehurek.com/gensim/tutorial.htm l 36
  37. 37. Thank You! • For questions and comments: - ann at auberonsolutions dot com 37

×