1. POS TAGGING AND HMM Tim Teks Mining Adapted from Heng Ji

2. Outline POS Tagging and HMM

3. 3/39 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, … …

4. 4/39 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.

5. 5/39 7 Traditional POS Categories Nnounchair, bandwidth, pacing Vverbstudy, debate, munch ADJadjpurple, tall, ridiculous ADVadverbunfortunately, slowly, Pprepositionof, by, to PROpronounI, me, mine DETdeterminerthe, a, that, those

6. 6/39 POS Tagging The process of assigning a part-of-speech or lexical class marker to each word in a collection. WORD tag theDET koalaN put V the DET keysN onP theDET tableN

7. 7/39 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

8. 8/39 Penn Treebank Tagset

9. 9/39 Why POS tagging is useful? Speech synthesis: How to pronounce “ lead ” ? INsult inSULT OBject obJECT OVERflow overFLOW DIScountdisCOUNT 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

10. 10/39 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!

11. 11/39 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, John walked 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)

12. 12/39 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, …

13. 13/39 Prepositions from CELEX

14. 14/39 English Particles

15. 15/39 Conjunctions

16. 16/39 POS Tagging Choosing 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

17. 17/39 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”.

18. 18/39 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

19. 19/39 How Hard is POS Tagging? Measuring Ambiguity

20. 20/39 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?

21. 21/39 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

22. 22/39 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

23. 23/39 How to do it? History 19601970198019902000 Brown Corpus Created (EN-US) 1 Million Words Brown Corpus Tagged HMM Tagging (CLAWS) 93%-95% Greene and Rubin Rule Based - 70% LOB Corpus Created (EN-UK) 1 Million Words DeRose/Church Efficient HMM Sparse Data 95%+ British National Corpus (tagged by CLAWS) POS Tagging separated from other NLP Transformation Based Tagging (Eric Brill) Rule Based – 95%+ Tree-Based Statistics (Helmut Shmid) Rule Based – 96%+ Neural Network 96%+ Trigram Tagger (Kempe) 96%+ Combined Methods 98%+ Penn Treebank Corpus (WSJ, 4.5M) LOB Corpus Tagged

24. 24/39 Two Methods for POS Tagging 1. Rule-based tagging (ENGTWOL) 2. Stochastic 1. Probabilistic sequence models HMM (Hidden Markov Model) tagging MEMMs (Maximum Entropy Markov Models)

25. 25/39 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.

26. 26/39 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

27. 27/39 Start With a Dictionary she:PRP promised:VBN,VBD toTO back:VB, JJ, RB, NN the:DT bill: NN, VB Etc… for the ~100,000 words of English with more than 1 tag

28. 28/39 Assign Every Possible Tag NN RB VBNJJ VB PRPVBD TOVB DT NN Shepromised to back the bill

29. 29/39 Write Rules to Eliminate Tags Eliminate VBN if VBD is an option when VBN|VBD follows “ PRP” NN RB JJ VB PRPVBD TO VB DTNN Shepromisedtoback thebill VBN

30. 30/39 POS tagging The involvement of ion channels in B and T lymphocyte activation is DT NN IN NN NNS IN NN CC NN NN NN VBZ supported by many reports of changes in ion fluxes and membrane VBN IN JJ NNS IN NNS IN NN NNS CC NN ……………………………………………………………………………………. Machine Learning Algorithm training We demonstrate that … Unseen text We demonstrate PRP VBP that … IN

31. 31/39 Goal of POS Tagging  We want the best set of tags for a sequence of words (a sentence)  W — a sequence of words  T — a sequence of tags  Example: P((NN NN P DET ADJ NN) | (heat oil in a large pot)) Our Goal Our Goal

32. 32/39 But, the Sparse Data Problem … Rich Models often require vast amounts of data Count up instances of the string "heat oil in a large pot" in the training corpus, and pick the most common tag assignment to the string.. Too many possible combinations

33. 33/39 POS Tagging as Sequence Classification We are given a sentence (an “observation” or “sequence of observations”) Secretariat is expected to race tomorrow What is the best sequence of tags that corresponds to this sequence of observations? Probabilistic view: Consider all possible sequences of tags Out of this universe of sequences, choose the tag sequence which is most probable given the observation sequence of n words w 1 …w n.

34. 34/39 Getting to HMMs We want, out of all sequences of n tags t 1 …t n the single tag sequence such that P(t 1 …t n |w 1 …w n ) is highest. Hat ^ means “our estimate of the best one” Argmax x f(x) means “the x such that f(x) is maximized”

35. 35/39 Getting to HMMs This equation is guaranteed to give us the best tag sequence But how to make it operational? How to compute this value? Intuition of Bayesian classification: Use Bayes rule to transform this equation into a set of other probabilities that are easier to compute

36. 36/39 Reminder: Apply Bayes’ Theorem (1763) posteriorposterior priorprior likelihoodlikelihood marginal likelihood Reverend Thomas Bayes — Presbyterian minister (1702-1761) Our Goal: To maximize it!

37. 37/39 How to Count  P(W|T) and P(T) can be counted from a large hand-tagged corpus; and smooth them to get rid of the zeroes

38. 38/39 Count P(W|T) and P(T)  Assume each word in the sequence depends only on its corresponding tag:

39. 39/39 Make a Markov assumption and use N-grams over tags... P(T) is a product of the probability of N-grams that make it up Make a Markov assumption and use N-grams over tags... P(T) is a product of the probability of N-grams that make it up Count P(T) historyhistory

40. 40/39 Part-of-speech tagging with Hidden Markov Models wordstags output probability transition probability

41. 41/39 Analyzing Fish sleep.

42. 42/39 A Simple POS HMM startnounverb end 0.8 0.2 0.8 0.7 0.1 0.2 0.1

43. 43/39 Word Emission Probabilities P ( word | state ) A two-word language: “ fish ” and “ sleep ” Suppose in our training corpus, “ fish ” appears 8 times as a noun and 5 times as a verb “ sleep ” appears twice as a noun and 5 times as a verb Emission probabilities: Noun P(fish | noun) :0.8 P(sleep | noun) :0.2 Verb P(fish | verb) :0.5 P(sleep | verb) :0.5

44. 44/39 Viterbi Probabilities

45. 45/39 startnounverb end 0.8 0.2 0.8 0.7 0.1 0.2 0.1

46. 46/39 startnounverb end 0.8 0.2 0.8 0.7 0.1 0.2 0.1 Token 1: fish

47. 47/39 startnounverb end 0.8 0.2 0.8 0.7 0.1 0.2 0.1 Token 1: fish

48. 48/39 startnounverb end 0.8 0.2 0.8 0.7 0.1 0.2 0.1 Token 2: sleep (if ‘fish’ is verb)

49. 49/39 startnounverb end 0.8 0.2 0.8 0.7 0.1 0.2 0.1 Token 2: sleep (if ‘fish’ is verb)

50. 50/39 startnounverb end 0.8 0.2 0.8 0.7 0.1 0.2 0.1 Token 2: sleep (if ‘fish’ is a noun)

51. 51/39 startnounverb end 0.8 0.2 0.8 0.7 0.1 0.2 0.1 Token 2: sleep (if ‘fish’ is a noun)

52. 52/39 startnounverb end 0.8 0.2 0.8 0.7 0.1 0.2 0.1 Token 2: sleep take maximum, set back pointers

53. 53/39 startnounverb end 0.8 0.2 0.8 0.7 0.1 0.2 0.1 Token 2: sleep take maximum, set back pointers

54. 54/39 startnounverb end 0.8 0.2 0.8 0.7 0.1 0.2 0.1 Token 3: end

55. 55/39 startnounverb end 0.8 0.2 0.8 0.7 0.1 0.2 0.1 Token 3: end take maximum, set back pointers

56. 56/39 startnounverb end 0.8 0.2 0.8 0.7 0.1 0.2 0.1 Decode: fish = noun sleep = verb

57. Markov Chain for a Simple Name Tagger START END PER X 0.3 0.2 0.3 0.6 0.5 George:0.3 W.:0.3 discussed:0.7 $:1.0 LOC 0.5 0.2 0.1 0.3 0.1 0.2 Bush:0.3 Iraq:0.1 George:0.2 Iraq:0.8 Transition Probability Emission Probability

58. 58/39 Exercise Tag names in the following sentence: George. W. Bush discussed Iraq.

59. 59/39 POS taggers Brill ’ s tagger http://www.cs.jhu.edu/~brill/ TnT tagger http://www.coli.uni-saarland.de/~thorsten/tnt/ Stanford tagger http://nlp.stanford.edu/software/tagger.shtml SVMTool http://www.lsi.upc.es/~nlp/SVMTool/ GENIA tagger http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/tagger/ More complete list at: http://www-nlp.stanford.edu/links/statnlp.html#Taggers