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POS Tagging: Introduction

POS Tagging: Introduction. Heng Ji hengji@cs.qc.cuny.edu Feb 2, 2008. Acknowledgement: some slides from Ralph Grishman, Nicolas Nicolov, J&M. Some Administrative Stuff. Assignment 1 due on Feb 17 Textbook: required for assignments and final exam. Outline. Parts of speech (POS) Tagsets

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POS Tagging: Introduction

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  1. POS Tagging: Introduction Heng Ji hengji@cs.qc.cuny.edu Feb 2, 2008 Acknowledgement: some slides from Ralph Grishman, Nicolas Nicolov, J&M

  2. Some Administrative Stuff • Assignment 1 due on Feb 17 • Textbook: required for assignments and final exam

  3. Outline • Parts of speech (POS) • Tagsets • POS Tagging • Rule-based tagging • Markup Format • Open source Toolkits

  4. What is Part-of-Speech (POS) • Generally speaking, Word Classes (=POS) : • Verb, Noun, Adjective, Adverb, Article, … • We can also include inflection: • Verbs: Tense, number, … • Nouns: Number, proper/common, … • Adjectives: comparative, superlative, … • …

  5. Parts of Speech • 8 (ish) traditional parts of speech • Noun, verb, adjective, preposition, adverb, article, interjection, pronoun, conjunction, etc • Called: parts-of-speech, lexical categories, word classes, morphological classes, lexical tags... • Lots of debate within linguistics about the number, nature, and universality of these • We’ll completely ignore this debate.

  6. 7 Traditional POS Categories • N noun chair, bandwidth, pacing • V verb study, debate, munch • ADJ adj purple, tall, ridiculous • ADV adverb unfortunately, slowly, • P preposition of, by, to • PRO pronoun I, me, mine • DET determiner the, a, that, those

  7. POS Tagging • The process of assigning a part-of-speech or lexical class marker to each word in a collection. WORD tag the DET koala N put V the DET keys N on P the DET table N

  8. Penn TreeBank POS Tag Set • Penn Treebank: hand-annotated corpus of Wall Street Journal, 1M words • 46 tags • Some particularities: • to /TO not disambiguated • Auxiliaries and verbs not distinguished

  9. Penn Treebank Tagset

  10. Why POS tagging is useful? • Speech synthesis: • How to pronounce “lead”? • INsult inSULT • OBject obJECT • OVERflow overFLOW • DIScount disCOUNT • CONtent conTENT • Stemming for information retrieval • Can search for “aardvarks” get “aardvark” • Parsing and speech recognition and etc • Possessive pronouns (my, your, her) followed by nouns • Personal pronouns (I, you, he) likely to be followed by verbs • Need to know if a word is an N or V before you can parse • Information extraction • Finding names, relations, etc. • Machine Translation

  11. Equivalent Problem in Bioinformatics • Durbin et al. Biological Sequence Analysis, Cambridge University Press. • Several applications, e.g. proteins • From primary structure ATCPLELLLD • Infer secondary structure HHHBBBBBC..

  12. Why is POS Tagging Useful? • First step of a vast number of practical tasks • Speech synthesis • How to pronounce “lead”? • INsult inSULT • OBject obJECT • OVERflow overFLOW • DIScount disCOUNT • CONtent conTENT • Parsing • Need to know if a word is an N or V before you can parse • Information extraction • Finding names, relations, etc. • Machine Translation

  13. Open and Closed Classes • Closed class: a small fixed membership • Prepositions: of, in, by, … • Auxiliaries: may, can, will had, been, … • Pronouns: I, you, she, mine, his, them, … • Usually function words (short common words which play a role in grammar) • Open class: new ones can be created all the time • English has 4: Nouns, Verbs, Adjectives, Adverbs • Many languages have these 4, but not all!

  14. Open Class Words • Nouns • Proper nouns (Boulder, Granby, Eli Manning) • English capitalizes these. • Common nouns (the rest). • Count nouns and mass nouns • Count: have plurals, get counted: goat/goats, one goat, two goats • Mass: don’t get counted (snow, salt, communism) (*two snows) • Adverbs: tend to modify things • Unfortunately, Johnwalked home extremely slowly yesterday • Directional/locative adverbs (here,home, downhill) • Degree adverbs (extremely, very, somewhat) • Manner adverbs (slowly, slinkily, delicately) • Verbs • In English, have morphological affixes (eat/eats/eaten)

  15. Closed Class Words Examples: • prepositions: on, under, over, … • particles: up, down, on, off, … • determiners: a, an, the, … • pronouns: she, who, I, .. • conjunctions: and, but, or, … • auxiliary verbs: can, may should, … • numerals: one, two, three, third, …

  16. Prepositions from CELEX

  17. English Particles

  18. Conjunctions

  19. POS TaggingChoosing a Tagset • There are so many parts of speech, potential distinctions we can draw • To do POS tagging, we need to choose a standard set of tags to work with • Could pick very coarse tagsets • N, V, Adj, Adv. • More commonly used set is finer grained, the “Penn TreeBank tagset”, 45 tags • PRP$, WRB, WP$, VBG • Even more fine-grained tagsets exist

  20. Using the Penn Tagset • The/DT grand/JJ jury/NN commmented/VBD on/IN a/DT number/NN of/IN other/JJ topics/NNS ./. • Prepositions and subordinating conjunctions marked IN (“although/IN I/PRP..”) • Except the preposition/complementizer “to” is just marked “TO”.

  21. POS Tagging • Words often have more than one POS: back • The back door = JJ • On my back = NN • Win the voters back = RB • Promised to back the bill = VB • The POS tagging problem is to determine the POS tag for a particular instance of a word. These examples from Dekang Lin

  22. How Hard is POS Tagging? Measuring Ambiguity

  23. Current Performance • How many tags are correct? • About 97% currently • But baseline is already 90% • Baseline algorithm: • Tag every word with its most frequent tag • Tag unknown words as nouns • How well do people do?

  24. Quick Test: Agreement? • the students went to class • plays well with others • fruit flies like a banana DT: the, this, that NN: noun VB: verb P: prepostion ADV: adverb

  25. Quick Test • the students went to class DT NN VB P NN • plays well with others VB ADV P NN NN NN P DT • fruit flies like a banana NN NN VB DT NN NN VB P DT NN NN NN P DT NN NN VB VB DT NN

  26. 1960 1970 1980 1990 2000 How to do it? History Combined Methods 98%+ Trigram Tagger (Kempe) 96%+ DeRose/Church Efficient HMM Sparse Data 95%+ Tree-Based Statistics (Helmut Shmid) Rule Based – 96%+ Transformation Based Tagging (Eric Brill) Rule Based – 95%+ Greene and Rubin Rule Based - 70% HMM Tagging (CLAWS) 93%-95% Neural Network 96%+ LOB Corpus Tagged Brown Corpus Created (EN-US) 1 Million Words Brown Corpus Tagged British National Corpus (tagged by CLAWS) POS Tagging separated from other NLP LOB Corpus Created (EN-UK) 1 Million Words Penn Treebank Corpus (WSJ, 4.5M)

  27. Two Methods for POS Tagging • Rule-based tagging • (ENGTWOL) • Stochastic • Probabilistic sequence models • HMM (Hidden Markov Model) tagging • MEMMs (Maximum Entropy Markov Models)

  28. Rule-Based Tagging • Start with a dictionary • Assign all possible tags to words from the dictionary • Write rules by hand to selectively remove tags • Leaving the correct tag for each word.

  29. Rule-based taggers • Early POS taggers all hand-coded • Most of these (Harris, 1962; Greene and Rubin, 1971) and the best of the recent ones, ENGTWOL (Voutilainen, 1995) based on a two-stage architecture • Stage 1: look up word in lexicon to give list of potential POSs • Stage 2: Apply rules which certify or disallow tag sequences • Rules originally handwritten; more recently Machine Learning methods can be used

  30. Start With a Dictionary • she: PRP • promised: VBN,VBD • to TO • back: VB, JJ, RB, NN • the: DT • bill: NN, VB • Etc… for the ~100,000 words of English with more than 1 tag

  31. Assign Every Possible Tag NN RB VBNJJ VB PRP VBD TO VB DT NN She promised to back the bill

  32. Write Rules to Eliminate Tags Eliminate VBN if VBD is an option when VBN|VBD follows “<start> PRP” NN RB JJ VB PRP VBD TO VB DT NN She promised to back the bill VBN

  33. Stage 1 of ENGTWOL Tagging • First Stage: Run words through FST morphological analyzer to get all parts of speech. • Example: Pavlov had shown that salivation … Pavlov PAVLOV N NOM SG PROPERhad HAVE V PAST VFIN SVO HAVE PCP2 SVOshown SHOW PCP2 SVOO SVO SVthat ADV PRON DEM SG DET CENTRAL DEM SGCSsalivation N NOM SG

  34. Stage 2 of ENGTWOL Tagging • Second Stage: Apply NEGATIVE constraints. • Example: Adverbial “that” rule • Eliminates all readings of “that” except the one in • “It isn’t that odd” Given input: “that”If(+1 A/ADV/QUANT) ;if next word is adj/adv/quantifier (+2 SENT-LIM) ;following which is E-O-S (NOT -1 SVOC/A) ; and the previous word is not a ; verb like “consider” which ; allows adjective complements ; in “I consider that odd” Then eliminate non-ADV tagsElse eliminate ADV

  35. Inline Mark-up • POS Tagging http://nlp.cs.qc.cuny.edu/wsj_pos.zip • Input Format Pierre Vinken, 61/CD years/NNS old , will join the board as a nonexecutive director Nov. 29. • Output Format 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 ./.

  36. POS Tagging Tools • NYU Prof. Ralph Grishman’s HMM POS tagger (in Java) http://nlp.cs.qc.cuny.edu/jet.zip http://nlp.cs.qc.cuny.edu/jet_src.zip http://www.cs.nyu.edu/cs/faculty/grishman/jet/license.html • Demo • How it works: Learned HMM: data/pos_hmm.txt Source code: src/jet/HMM/HMMTagger.java

  37. POS Tagging Tools • Stanford tagger (Loglinear tagger )http://nlp.stanford.edu/software/tagger.shtml • Brill tagger • http://www.tech.plym.ac.uk/soc/staff/guidbugm/software/RULE_BASED_TAGGER_V.1.14.tar.Z • tagger LEXICON test BIGRAMS LEXICALRULEFULE CONTEXTUALRULEFILE • YamCha (SVM) http://chasen.org/~taku/software/yamcha/ • MXPOST (Maximum Entropy) ftp://ftp.cis.upenn.edu/pub/adwait/jmx/ • More complete list at: http://www-nlp.stanford.edu/links/statnlp.html#Taggers

  38. NLP Toolkits • Uniform CL Annotation Platform • UIMA (IBM NLP platform): http://incubator.apache.org/uima/svn.html • Mallet (UMASS): http://mallet.cs.umass.edu/index.php/Main_Page • MinorThird (CMU): http://minorthird.sourceforge.net/ • NLTK: http://nltk.sourceforge.net/ Natural langauge toolkit, with data sets  Demo • Information Extraction • Jet (NYU IE toolkit) http://www.cs.nyu.edu/cs/faculty/grishman/jet/license.html • Gate: http://gate.ac.uk/download/index.htmlUniversity of Sheffield IE toolkit • Information Retrieval • INDRI: http://www.lemurproject.org/indri/Information Retrieval toolkit • Machine Translation • Compara: http://adamastor.linguateca.pt/COMPARA/Welcome.html • ISI decoder: http://www.isi.edu/licensed-sw/rewrite-decoder/ • MOSES: http://www.statmt.org/moses/

  39. Looking Ahead: Next Class • Machine Learning for POS Tagging: Hidden Markov Model

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