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Fall 2005 Lecture Notes #8

EECS 595 / LING 541 / SI 661. Natural Language Processing. Fall 2005 Lecture Notes #8. Evaluation of NLP systems. The classical pipeline (for supervised learning). Training set/dev set/test set Dumb baseline Intelligent baseline Your algorithm Human ceiling Accuracy/precision/recall

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Fall 2005 Lecture Notes #8

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  1. EECS 595 / LING 541 / SI 661 Natural Language Processing Fall 2005 Lecture Notes #8

  2. Evaluation of NLP systems

  3. The classical pipeline (for supervised learning) • Training set/dev set/test set • Dumb baseline • Intelligent baseline • Your algorithm • Human ceiling • Accuracy/precision/recall • Multiple references • Statistical significance

  4. Special cases • Document retrieval systems • Part of speech tagging • Parsing • Labeled recall • Labeled precision • Crossing brackets

  5. Word classes andpart-of-speech tagging

  6. Part of speech tagging • Problems: transport, object, discount, address • More problems: content • French: est, président, fils • “Book that flight” – what is the part of speech associated with “book”? • POS tagging: assigning parts of speech to words in a text. • Three main techniques: rule-based tagging, stochastic tagging, transformation-based tagging

  7. Rule-based POS tagging • Use dictionary or FST to find all possible parts of speech • Use disambiguation rules (e.g., ART+V) • Typically hundreds of constraints can be designed manually

  8. Example in French <S> ^ beginning of sentence La rf b nms u article teneur nfs nms noun feminine singular Moyenne jfs nfs v1s v2s v3s adjective feminine singular en p a b preposition uranium nms noun masculine singular des p r preposition rivi`eres nfp noun feminine plural , x punctuation bien_que cs subordinating conjunction délicate jfs adjective feminine singular À p preposition calculer v verb

  9. Sample rules BS3 BI1: A BS3 (3rd person subject personal pronoun) cannot be followed by a BI1 (1st person indirect personal pronoun). In the example: ``il nous faut'' ({\it we need}) - ``il'' has the tag BS3MS and ``nous'' has the tags [BD1P BI1P BJ1P BR1P BS1P]. The negative constraint ``BS3 BI1'' rules out ``BI1P'', and thus leaves only 4 alternatives for the word ``nous''. N K: The tag N (noun) cannot be followed by a tag K (interrogative pronoun); an example in the test corpus would be: ``... fleuve qui ...'' (...river, that...). Since ``qui'' can be tagged both as an ``E'' (relative pronoun) and a ``K'' (interrogative pronoun), the ``E'' will be chosen by the tagger since an interrogative pronoun cannot follow a noun (``N''). R V:A word tagged with R (article) cannot be followed by a word tagged with V (verb): for example ``l' appelle'' (calls him/her). The word ``appelle'' can only be a verb, but ``l''' can be either an article or a personal pronoun. Thus, the rule will eliminate the article tag, giving preference to the pronoun.

  10. Confusion matrix Most confusing: NN vs. NNP vs. JJ, VBD vs. VBN vs. JJ

  11. HMM Tagging • T = argmax P(T|W), where T=t1,t2,…,tn • By Bayes’s theorem: P(T|W) = P(T)P(W|T)/P(W) • Thus we are attempting to choose the sequence of tags that maximizes the rhs of the equation • P(W) can be ignored • P(T)P(W|T) = ? • P(T) is called the prior, P(W|T) is called the likelihood.

  12. HMM tagging (cont’d) • P(T)P(W|T) = P(wi|w1t1…wi-1ti-1ti)P(ti|t1…ti-2ti-1) • Simplification 1: P(W|T) = P(wi|ti) • Simplification 2: P(T)= P(ti|ti-1) • T = argmax P(T|W) = argmax P(wi|ti) P(ti|ti-1)    

  13. Estimates • P(NN|DT) = C(DT,NN)/C(DT)=56509/116454 = .49 • P(is|VBZ = C(VBZ,is)/C(VBZ)=10073/21627=.47

  14. Example • Secretariat/NNP is/VBZ expected/VBN to/TO race/VB tomorrow/NR • People/NNS continue/VBP to/TO inquire/VB the/AT reason/NN for/IN the/AT race/NN for/IN outer/JJ space/NN • TO: to+VB (to sleep), to+NN (to school)

  15. Example NNP VBZ VBN TO VB NR Secretariat is expected to race tomorrow NNP VBZ VBN TO NN NR Secretariat is expected to race tomorrow

  16. Example (cont’d) • P(NN|TO) = .00047 • P(VB|TO) = .83 • P(race|NN) = .00057 • P(race|VB) = .00012 • P(NR|VB) = .0027 • P(NR|NN) = .0012 • P(VB|TO)P(NR|VB)P(race|VB) = .00000027 • P(NN|TO)P(NR|NN)P(race|NN) = .00000000032

  17. Decoding • Finding what sequence of states is the source of a sequence of observations • Viterbi decoding (dynamic programming) – finding the optimal sequence of tags • Input: HMM and sequence of words, output: sequence of states

  18. Transformation-based learning • P(NN|race) = .98 • P(VB|race) = .02 • Change NN to VB when the previous tag is TO • Types of rules: • The preceding (following) word is tagged z • The word two before (after) is tagged z • One of the two preceding (following) words is tagged z • One of the three preceding (following) words is tagged z • The preceding word is tagged z and the following word is tagged w

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