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LING 138/238 SYMBSYS 138 Intro to Computer Speech and Language Processing

LING 138/238 SYMBSYS 138 Intro to Computer Speech and Language Processing. Lecture 6: Part of Speech Tagging (II): October 14, 2004 Neal Snider. Thanks to Dan Jurafsky, Jim Martin, Dekang Lin, and Bonnie Dorr for some of the examples and details in these slides!.

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LING 138/238 SYMBSYS 138 Intro to Computer Speech and Language Processing

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  1. LING 138/238 SYMBSYS 138Intro to Computer Speech and Language Processing Lecture 6: Part of Speech Tagging (II): October 14, 2004 Neal Snider Thanks to Dan Jurafsky, Jim Martin, Dekang Lin, and Bonnie Dorr for some of the examples and details in these slides! LING 138/238 Autumn 2004

  2. Week 3: Part of Speech tagging • Part of speech tagging • Parts of speech • What’s POS tagging good for anyhow? • Tag sets • Rule-based tagging • Statistical tagging • “TBL” tagging LING 138/238 Autumn 2004

  3. 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. LING 138/238 Autumn 2004

  4. 3 methods for POS tagging • Rule-based tagging • (ENGTWOL) • Stochastic (=Probabilistic) tagging • HMM (Hidden Markov Model) tagging • Transformation-based tagging • Brill tagger LING 138/238 Autumn 2004

  5. Statistical Tagging • Based on probability theory • Today we’ll go over a few basic ideas of probability theory • Then we’ll do HMM and TBL tagging. LING 138/238 Autumn 2004

  6. Probability and part of speech tags • What’s the probability of drawing a 2 from a deck of 52 cards with four 2s? • What’s the probability of a random word (from a random dictionary page) being a verb? LING 138/238 Autumn 2004

  7. Probability and part of speech tags • What’s the probability of a random word (from a random dictionary page) being a verb? • How to compute each of these • All words = just count all the words in the dictionary • # of ways to get a verb: number of words which are verbs! • If a dictionary has 50,000 entries, and 10,000 are verbs…. P(V) is 10000/50000 = 1/5 = .20 LING 138/238 Autumn 2004

  8. Probability and Independent Events • What’s the probability of picking two verbs randomly from the dictionary • Events are independent, so multiply probs P(w1=V,w2=V) = P(V) * P(V) = 1/5 * 1/5 = 0.04 • What if events are not independent? LING 138/238 Autumn 2004

  9. Conditional Probability • Written P(A|B). • Let’s say A is “it’s raining”. • Let’s say P(A) in drought-stricken California is .01 • Let’s say B is “it was sunny ten minutes ago” • P(A|B) means “what is the probability of it raining now if it was sunny 10 minutes ago” • P(A|B) is probably way less than P(A) • Let’s say P(A|B) is .0001 LING 138/238 Autumn 2004

  10. Conditional Probability and Tags • P(Verb) is the probability of a randomly selected word being a verb. • P(Verb|race) is “what’s the probability of a word being a verb given that it’s the word “race”? • Race can be a noun or a verb. • It’s more likely to be a noun. • P(Verb|race) can be estimated by looking at some corpus and saying “out of all the times we saw ‘race’, how many were verbs? • In Brown corpus, P(Verb|race) = 96/98 = .98 • How to calculate for a tag sequence, say P(NN|DT)? LING 138/238 Autumn 2004

  11. Stochastic Tagging • Based on probability of certain tag occurring given various possibilities • Necessitates a training corpus • No probabilities for words not in corpus. • Training corpus may be too different from test corpus. LING 138/238 Autumn 2004

  12. Stochastic Tagging (cont.) Simple Method: Choose most frequent tag in training text for each word! • Result: 90% accuracy • Why? • Baseline: Others will do better • HMM is an example LING 138/238 Autumn 2004

  13. HMM Tagger • Intuition: Pick the most likely tag for this word. • HMM Taggers choose tag sequence that maximizes this formula: • P(word|tag) × P(tag|previous n tags) • Let T = t1,t2,…,tnLet W = w1,w2,…,wnFind POS tags that generate a sequence of words, i.e., look for most probable sequence of tags T underlying the observed words W. LING 138/238 Autumn 2004

  14. HMM Tagger • argmaxT P(T|W) • argmaxTP(W|T)P(T) Bayes Rule • argmaxTP(w1…wn|t1…tn)P(t1…tn) Remember, we are trying to find the sequence T that will maximize P(T|W) so this equation is calculated over the whole sentence. LING 138/238 Autumn 2004

  15. HMM Tagger • Assume word is dependent only on its own POS tag: it is independent of the others around it • argmaxT[P(w1|t1)P(w2|t2)…P(wn|tn)][P(t1)P(t2|t1)…P(tn|tn-1)] LING 138/238 Autumn 2004

  16. Bigram HMM Tagger • Also assume that probability is dependent only on previous tag • For each word and possible tag, need to calculate: P(ti) = P(wi|ti)P(ti|ti-1) then multiply this over each possible tag and each word over the sequence of tags LING 138/238 Autumn 2004

  17. Bigram HMM Tagger • How do we compute P(ti|ti-1)? • c(ti-1ti)/c(ti-1) • How do we compute P(wi|ti)? • c(wi,ti)/c(ti) • How do we compute the most probable tag sequence? • Viterbi algorithm LING 138/238 Autumn 2004

  18. An Example • Secretariat/NNP is/VBZ expected/VBN to/TO race/VB tomorrow/NN • People/NNS continue/VBP to/TO inquire/VB the DT reason/NN for/IN the/DT race/NN for/IN outer/JJ space/NN • to/TO race/???the/DT race/??? • ti = argmaxj P(tj|ti-1)P(wi|tj) • i = num of word in sequence, j = num among possible tags • max[P(VB|TO)P(race|VB) , P(NN|TO)P(race|NN)] • Brown: • P(NN|TO) = .021 × P(race|NN) = .00041 = .000007 • P(VB|TO) = .34 × P(race|VB) = .00003 = .00001 LING 138/238 Autumn 2004

  19. Viterbi Algorithm S1 S2 S3 S4 S5 LING 138/238 Autumn 2004

  20. Transformation-Based Tagging (Brill Tagging) • Combination of Rule-based and stochastic tagging methodologies • Like rule-based because rules are used to specify tags in a certain environment • Like stochastic approach because machine learning is used—with tagged corpus as input • Input: • tagged corpus • dictionary (with most frequent tags) LING 138/238 Autumn 2004

  21. Transformation-Based Tagging (cont.) • Basic Idea: • Set the most probable tag for each word as a start value • Change tags according to rules of type “if word-1 is a determiner and word is a verb then change the tag to noun” in a specific order • Training is done on tagged corpus: • Write a set of rule templates • Among the set of rules, find one with highest score • Continue from 2 until lowest score threshold is passed • Keep the ordered set of rules • Rules make errors that are corrected by later rules LING 138/238 Autumn 2004

  22. TBL Rule Application • Tagger labels every word with its most-likely tag • For example: race has the following probabilities in the Brown corpus: • P(NN|race) = .98 • P(VB|race)= .02 • Transformation rules make changes to tags • “Change NN to VB when previous tag is TO”… is/VBZ expected/VBN to/TO race/NN tomorrow/NNbecomes… is/VBZ expected/VBN to/TO race/VB tomorrow/NN LING 138/238 Autumn 2004

  23. TBL: Rule Learning • 2 parts to a rule • Triggering environment • Rewrite rule • The range of triggering environments of templates (from Manning & Schutze 1999:363) Schema ti-3 ti-2 ti-1 ti ti+1 ti+2 ti+3 1 * 2 * 3 * 4 * 5 * 6 * 7 * 8 * 9 * LING 138/238 Autumn 2004

  24. TBL: The Tagging Algorithm • Step 1: Label every word with most likely tag (from dictionary) • Step 2: Check every possible transformation & select one which most improves tagging • Step 3: Re-tag corpus applying the rules • Repeat 2-3 until some criterion is reached, e.g., X% correct with respect to training corpus • RESULT: Sequence of transformation rules LING 138/238 Autumn 2004

  25. TBL: Rule Learning (cont.) • Problem: Could apply transformations ad infinitum! • Constrain the set of transformations with “templates”: • Replace tag X with tag Y, provided tag Z or word Z’ appears in some position • Rules are learned in ordered sequence • Rules may interact. • Rules are compact and can be inspected by humans LING 138/238 Autumn 2004

  26. Templates for TBL LING 138/238 Autumn 2004

  27. TBL: Problems • First 100 rules achieve 96.8% accuracyFirst 200 rules achieve 97.0% accuracy • Execution Speed: TBL tagger is slower than HMM approach • Learning Speed: Brill’s implementation over a day (600k tokens) BUT … (1) Learns small number of simple, non-stochastic rules (2) Can be made to work faster with FST (3) Best performing algorithm on unknown words LING 138/238 Autumn 2004

  28. Tagging Unknown Words • New words added to (newspaper) language 20+ per month • Plus many proper names … • Increases error rates by 1-2% • Method 1: assume they are nouns • Method 2: assume the unknown words have a probability distribution similar to words only occurring once in the training set. • Method 3: Use morphological information, e.g., words ending with –ed tend to be tagged VBN. LING 138/238 Autumn 2004

  29. Using Morphological Information LING 138/238 Autumn 2004

  30. Evaluation • The result is compared with a manually coded “Gold Standard” • Typically accuracy reaches 96-97% • This may be compared with result for a baseline tagger (one that uses no context). • Important: 100% is impossible even for human annotators. LING 138/238 Autumn 2004

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