1 / 27

Part-of-Speech Tagging

Part-of-Speech Tagging. 인공지능 연구실 정 성 원. The beginning. The task of labeling (or tagging) each word in a sentence with its appropriate part of speech. The representative put chairs on the table AT NN VBD NNS IN AT NN Using Brown/Penn tag sets

jaguar
Download Presentation

Part-of-Speech Tagging

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Part-of-Speech Tagging 인공지능 연구실 정 성 원

  2. The beginning • The task of labeling (or tagging) each word in a sentence with its appropriate part of speech. • The representative put chairs on the table AT NN VBD NNS IN AT NN • Using Brown/Penn tag sets • A problem of limited scope • Instead of constructing a complete parse • Fix the syntactic categories of the word in a sentence • Tagging is a limited but useful application. • Information extraction • Question and answering • Shallow parsing

  3. The Information Sources in Tagging • Syntagmatic: look at the tags assigned to nearby words; some combinations are highly likely while others are highly unlikely or impossible • ex) a new play • AT JJ NN • AT JJ VBP • Lexical : look at the word itself. (90% accuracy just by picking the most likely tag for each word) • Verb is more likely to be a noun than a verb

  4. Notation • wi the word at position i in the corpus • ti the tag of wi • wi,i+m the words occurring at positions i through i+m • ti,i+m the tags ti … ti+m for wi … wi+m • wl the lth word in the lexicon • tj the jth tag in the tag set • C(wl) the number of occurrences of wl in the training set • C(tj) the number of occurrences of tj in the training set • C(tj,tk) the number of occurrences of tj followed by tk • C(wl,tj) the number of occurrences of wl that are tagged as tj • T number of tags in tag set • W number of words in the lexicon • n sentence length

  5. The Probabilistic Model (I) • The sequence of tags in a text as Markov chain. • A word’s tag only depends on the previous tag (Limited horizon) • Dependency does not change over time (Time invariance) • compact notation : Limited Horizon Property

  6. The Probabilistic Model (II) • Maximum likelihood estimate tag following

  7. The Probabilistic Model (III) (We define P(t1|t0)=1.0 to simplify our notation) • The final equation

  8. The Probabilistic Model (III) • Algorithm for training a Visible Markov Model Tagger Syntagmatic Probabilities: for all tags tjdo for all tags tkdo P(tk | tj)=C(tj, tk)/C(tj) end end Lexical Probabilities: for all tags tjdo for all words wldo P(wl | tj)=C(wl, tj)/C(tj) end end

  9. The Probabilistic Model (IV) <Idealized counts of some tag transitions in the Brown Corpus>

  10. The Probabilistic Model (V) <Idealized counts for the tags that some words occur with in the Brown Corpus>

  11. The Viterbi algorithm comment : Given: a sentence of length n comment : Initialization δ1(PERIOD) = 1.0 δ1(t) = 0.0 for t ≠ PERIOD comment : Induction for i := 1 to n step 1 do for all tags tj do δi+1(tj) := max1<=k<=T[δi(tk)*P(wi+1|tj)*P(tj|tk)] ψi+1(tj) := argmax1<=k<=T[δi(tk)*P(wi+1|tj)*P(tj|tk)] end end comment : Termination and path-readout Xn+1 = argmax1<=j<=Tδn+1(j) for j := n to 1 step – 1 do Xj = ψj+1(Xj+1) end P(X1 , … , Xn) = max1<=j<=Tδn+1(tj)

  12. Variations (I) • Unknown words • Unknown words are a major problem for taggers • The simplest model for unknown words • Assume that they can be of any part of speech • Use morphological information • Past tense form : words ending in –ed • Capitalized

  13. Variations (II) • Trigram taggers • The basic Markov Model tagger = bigram tagger • two tag memory • disambiguate more cases • Interpolation and variable memory • trigram tagger may make worse pridictions than a bigram tagger • linear interpolation • Variable Memory Markov Model

  14. Variations (III) • Smoothing • Reversibility • Markov model decodes from left to right = decodes from right to left Kl is the number of possible parts of speech of wl

  15. Variations (IV) • Maximum Likelihood: Sequence vs. tag by tag • Viterbi Alogorithm : maximize P(t1,n|w1,n) • Consider : maximize P(ti|w1,n) • for all i which amounts to summing over different tag sequance • ex) Time flies like a arrow. • a. NN VBZ RB AT NN. P(.) = 0.01 • b. NN NNS VB AT NN. P(.) = 0.01 • c. NN NNS RB AT NN. P(.) = 0.001 • d. NN VBZ VB AT NN. P(.) = 0 • one error does not affect the tagging of other words

  16. Applying HMMs to POS tagging(I) • If we have no training data, we can use a HMM to learn the regularities of tag sequences. • HMM consists of the following elements • a set of states ( = tags ) • an output alphabet ( words or classes of words ) • initial state probabilities • state transition probabilities • symbol emission probabilities

  17. Applying HMMs to POS tagging(II) • Jelinek’s method • bj.l : probability that word (or word class) l is emitted by tag j

  18. Applying HMMs to POS tagging(III) • Kupiec’s method |L| is the number of indices in L

  19. Transformation-Based Learning of Tags • Markov assumption are too crude→ transformation-based tagging • Exploit a wider range • An order of magnitude fewer decisions • Two key components • a specification of which ‘error-correcting’ transformations are admissible • The learning algorithm

  20. Transformation(I) • A triggering environment • A rewrite rule • Form t1→t2 : replace t1 by t2

  21. Transformation(II) • environments can be conditioned • combination of words and tags • Morphology-triggered transformation • ex) Replace NN by NNS if the unknown word’s suffix is -s

  22. The learning algorithm C0 := corpus with each word tagged with its most frequent tag for k:=0 step 1 do ν:=the transformation ui that minimizes E(ui(Ck)) if (E(Ck)-E(ν(Ck))) < Єthen break fi Ck+1:= ν(Ck) τk+1:= ν end Output sequence: τ1, …, τk

  23. Relation to other models • Decision trees • similarity with Transformation-based learning • a series of relableing • difference with Transformation-based learning • split at each node in a decision tree • different sequence of transformation for each node • Probabilistic models in general

  24. Automata • Transformation-based tagging has a rule component, it also has a quantitative component. • Once learning is complete, transformation-based tagging is purely symbolic • Transformation-based tagger can be converted into another symbolic object • Roche and Schobes(1995) : finite state transducer • Advantage : speed

  25. Other Method, Other Languages • Other approaches to tagging • In chapter 16 • Languages other than English • In many other languages, word order is much freer • The rich inflections of a word contribute more information about part of speech

  26. Tagging accuracy • 95%~97% when calculated over all words • Considerable factors • The amount of training data available • The tag set • The difference between training set and test set • Unknown words • a ‘dump’ tagger • Always chooses a word’s most frequent tag • Accuracy of about 90% • EngCG

  27. Applications of tagging • Benefit from syntactically disambiguated text • Partial Parsing • Finding none phrases of sentence • Information Extraction • Finding value for the predefined slots of a template • Finding good indexing term in information retrieval • Question Answering • Returning an appropriate noun such as a location, a person, or a date

More Related