Natural Language Processing
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1 Natural Language Processing Tagging Based on slides from Michael Collins, Yoav Goldberg, Yoav Artzi
2
3 Plan What are part-of-speech tags? What are tagging models? Models for tagging
4 Model zoo Tagging Parsing Generative HMMs PCFGs Greedy Transitionbased parsing Log-linear Locally normalized Viterbi CKY Globally normalized forwardbackward Inside-outside And then also deep learning variants!
5 Part-of-speech tags The/DT stuffy/jj room/nn is/aux filled/vbn with/in students/nns
6 Cross-language variations Some languages don t have adjectives (use verbs instead) I see well : eye me-clear Some language have noun classifiers Mandarin ( three long and flat knives ) Thai
7 What is it good for Characterizes the behavior of a word in context Useful for downstream applications/tasks Parsing what are parse trees good for? Question answering (if the question is when then the answer should contain a numeral) Articulation (object/object) Punctuation (Hebrew) But with a lot of data can often do without it.
8 How to determine what it is Morphology -ly ה הידיעה Can you add possesives? Context Appears after the Before a verb The yinkish dripner blorked quastofically into the nindin with the pidbis.
9 How to determine what it is The yinkish dripner blorked quastofically into the nindin with the pidibs. yinkish: adjective (or noun?) dripner: noun blorked: verb quastofically: adverb nindin: noun pidibs: noun
10 conjunction >preposition I study nlp because I like science I study nlp because science
11 Nouns In English: Take s, ness, ment Occur after the Refer to objects and entities in the real world But also smoking is forbidden Types: Proper nouns: Israel, Tel-Aviv University Common nouns Count nouns: classroom Mass nouns: sand
12 Verbs Usually describe actions, states and processes eat, talk Can have rich morphology (tense, aspect, )
13 Adjectives and adverbs Adjectives: Describe a noun, a property of some entity or object (color, texture, state, ) Adverbs: Describe an action (direction, degree, manner, time, location)
14 Prepositions vs. particles Prepositions on the table Particles: interested in music
15 Penn Treebank tag set
16 Universal tag set (>20 languages)
17 Part-of-speech tagging Input: Profits soared at Boeing Co., easily topping forecasts on Wall Street, as their CEO Alan Mulally announced first quarter results. Output: Profits/N soared/v at/p Boeing/N Co./N,/, easily/adv topping/v forecasts/n on/p Wall/N Street/N,/, as/p their/poss CEO/N Alan/N Mulally/N announced/v first/adj quarter/n results/n./. N: noun V: verb P: preposition Adv: adverb Adj: adjective Why is this hard?
18 Information sources Local: can is usually a modal verb (MD) but is sometimes a noun (N) Contextual a noun is more likely than a verb after a noun Conflict: DT NN is common but DT VB is not The trash can is in the garage
19 Distribution of POS tags Brown corpus #tags #word types most common tag baseline gets ~90% SOTA: 97% one error per 30 words
20 Brill, 1995 Transformation-based tagger Start with most frequent tag per word Then apply rules that change it based on context If VB and DT before change VB to NN Learn from data which rules work well
21 Named entity recognition Input: Profits soared at Boeing Co., easily topping forecasts on Wall Street, as their CEO Alan Mulally announced first quarter results. Output: Profits soared at [Boeing Co.]org, easily topping forecasts on [Wall Street]loc, as their CEO [Alan Mulally]per announced first quarter results.
22 NER as tagging Input: Profits soared at Boeing Co., easily topping forecasts on Wall Street, as their CEO Alan Mulally announced first quarter results. Output: Profits/O soared/o at/o Boeing/B-org Co./I-org,/O easily/o topping/o forecasts/o on/o Wall/B-loc Street/I-loc,/O as/o their/o CEO/O Alan/B-per Mulally/I-per announced/o first/o quarter/o results/ O./O
23 Chunking NER is an example for chunking, which is useful in general information extraction Find companies Diseases Genes
24 Training set Learn a function from sentences to tag sequences
25 Supervised learning Input: sentence-tag sequence pairs {x (i),y (i) } m i=1 Output: function f from sentence x to tag sequence y Example: x (i) = the dog laughs y (i) = DT NN VBZ
26 Structured prediction The size of the output space depends on the size of the input Compare to language modeling Not binary nor multi-class The size of the output is exponential in the size of the input What is the size of the output space?
27 Conditional model Learn from data a model for p(y x) Given a sentence x return: arg max y p(y x) But how do you define a model p(y x)?
28 Generative model Learn a model over p(x, y) Express p(x, y) = p(y)p(x y) Generative story: generate tags, and then produce words from tags p(y): prior on tags (generate a sequence of tags) p(x y): data conditioned on tags (generate words from tags) Use Bayes rule to express p(y x): p(y x) = p(y)p(x y) P y p(y 0 )p(x y 0 ) 0 Output: f(x) = arg max y p(y x) = arg max y p(y)p(x y) Py 0 p(y 0 )p(x y 0 ) = arg max y p(y)p(x y)
29 Hidden Markov Models Goal: define a tagging model p(x, y) = p(y)p(x y) p(y) is a trigram model over tag sequences Model tag transitions p(y) = n+1 Y i=1 q(y i y i 2,y i 1 ), y 0 = y 1 =,y n+1 =STOP
30 Hidden Markov Models p(x y) models emissions Assumption: a word depends only on its tag (is this reasonable?) p(x y) =p(x 1 y) p(x 2 x 1,y)... p(x n x 1,...,x n 1,y) ny = i=1 p(x i x 1,...,x i 1,y) ny e(x i y i ) i=1
31 Hidden Markov Models p(x 1,...,x n,y 1...,y n, STOP) = ny q(stop y n y 0 = y 1 = 1,y n ) i=1 q(y i y i 2,y i 1 ) e(x i y i ) y-1 y0 y1 y2 y3 y4 y5 x1 x2 x3 x4
32 Example p(the, dog, laughs, DT, N, V) =q(dt *, *) q(n *, DT) q(v DT, N) q(stop N, V) e(the DT) e(dog V) e(laughs V) In practice work in log space
33 Parameter estimation Transition parameters: Get Maximum likelihood (ML) from training data (like language models) q(y i y i 2,y i 1 )= c(y i 2,y i 1,y i ) c(y i 2,y i 1 ) Smoothing to handle rare tag trigrams and bigrams q(y i y i 2,y i 1 )= 1 c(y i 2,y i 1,y i ) c(y i 2,y i 1 ) + X i 2 c(y i 1,y i ) c(y i 1 ) + 3 c(y i) M i =1, i 0
34 Parameter estimation Emission parameters Again, ML estimates from training data e(x y) = c(x, y) c(y) Can we have zero counts in the test?
35 Rare words problems Profits soared at Boeing Co., easily topping forecasts on Wall Street, as their CEO Alan Mulally announced first quarter results. We encounter new words frequently in real text Names, rare words, types, etc. etc. p(y x) = 0 for all y
36 Common solution Partition the vocabulary to two sets frequent: occurring more than K(=5) times rare: other words Cluster rare words to a small number of clusters manually Preprocess the training data to replace rare words with their cluster ID.
37 Bickel et al., 1999 Example (named entity recognition) Class Example Number 1234 NumberAndSlash 29/3/2017 allcaps initcap NLP Jonathan firstword lowercase table other ;
38 Example (named entity recognition) Input: Profits soared at Boeing Co., easily topping forecasts on Wall Street, as their CEO Alan Mulally announced first quarter results. Output: firstword/o soared/o at/o initcap/b-org Co./I-org,/ O easily/o lowercase/o forecasts/o on/o initcap/bloc Street/I-loc,/O as/o their/o CEO/O Alan/B-per initcap/i-per announced/o first/o quarter/o results/o./o
39 Inference/decoding
40 The problem The decoding problem is to compute argmaxy p(x, y) = argmaxy p(y x) This is necessary in HMMs at test time
41 Brute force Score all possible tag sequences y With sequences of length n and and a tag set S this take exponential time S x S = S n
42 Greedy choose y 1 that maximizes e(x 1 y 1 )q(y 1 *, *) choose y 2 that maximizes e(x 2 y 2 )q(y 2 *, y 1 ) choose y n that maximizes e(x n y n )q(y n y n-2, y n-1 ) Complexity: O( S n ) but incorrect! Exercise: Build an example (with q and e parameters) where the greedy algorithm fails
43 Viterbi Efficient and correct algorithm Intuition: The HMM model decomposes the probability of the full tag sequence to a product of more local probabilities
44 Viterbi Let n be the length of the sentence, and S be the tag set Sk is the tag set for position k, -1 k n S 0 = S 1 = { }, S k = S Define the probability of a tag prefix: r(y 1,y 0,y 1,...,y k )= ky q(y i y i 2,y y 1 ) e(x i y i ) i=1
45 Viterbi Build a dynamic programming table for the highest tag sequence probability ending in a tag bigram at position k (k, u, v) = π(7, P, D): max {y 1,...,y k : y k 1 = u, y k = v} r(y 1,y 0,y 1,...,y k ) V V V V V N N N N N D D D D D * * P P P P P P D the man saw the dog with the telescope
46 Viterbi algorithm Base: π(0, *, *) = 1 (all tag sequences begin with those) Recursively: for k 2 {1...n}, for all u 2 S k 1,v 2 S k : (k, u, v) = max (k 1,w,u) q(v w, u) e(x k v) w2s k 2
47 Correctness: induction on k V V V V V N N N N N D D D D D * * P P P P P P D the man saw the dog with the telescope Claim: The highest probability tag sequence of length k ending with w,u,v must contain a highest probability tag sequence of length k-1 ending in w,u. Sketch: Assume the highest probability sequence y* ending in w,u,v of length k does not contain a highest probability tag sequence of length k-1 that ends in w,u. Then, we can construct a sequence ynew with higher probability (how?) contradicting the fact that y* has highest probability.
48 Example * * V V V V V N N N N N D D D D D P P P P P P D the man saw the dog with the telescope (7, P, DT) = max w2{dt,p,n,v} (6,w,P) q(dt w, P) e(the DT)
49 Viterbi with back pointers Input: a sentence x 1,...,x n, parameters q, e, and tag set S Base case: (0,, ) =1 Definition: S 1 = S 0 = { }, S k = S for k 2 {1...n} Algorithm: for k 2 {1...n},u2 S k 1,v 2 S k : (k, u, v) = max (k 1,w,u) q(v w, u) e(x k v) w2s k 2 bp(k, u, v) = arg max w2s k 2 (k 1,w,u) q(v w, u) e(x k v) (y n 1,y n ) = arg max( (n, u, v) q(stop u, v) u,v for k =(n 2)...1,y k =bp(k +2,y k+1,y k+2 ) return y 1,...,y n
50 Viterbi runtime and memory Memory: Dynamic programming table: O(n S 2 ) Runtime: For every position and tag pair do max over all tags: O(n S 3 ) Linear in n as opposed to exponential Correct
51 Popping up Training: estimate parameters q and e from training data Test: Apply Viterbi
52 Summary HMMs are simple to train: count stuff Reasonable performance for POS tagging 96.46% token accuracy on WSJ (link) Unknown words can be troublesome
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