Learning To Deal With Little Or No Annotated Data

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1 Learning To Deal With Little Or No Annotated Data Information Sciences Institute and Department of Computer Science University of Southern California 4676 Admiralty Way, Suite 1001 Marina del Rey, CA

2 Overview There is no better data than more data. Annotating data is more cost effective than writing rules manually. Still, annotating data is expensive How can we annotate as little data as possible? Active Learning Bootstrapping Co-training Unsupervised Learning. Pattern Discovery Hidden Variables (the EM algorithm) Corpus Exploitation for Summarization.

3 Choosing between confusables [Banko and Brill, ACL-2001] (two, too, to) (principal, principle) (then, than)

4 Base Noun Phrase Chunking [Ngai and Yarowsky, ACL-2000] Asked human judges to write rules that can be used to identify base noun phrases and automatically integrated those rules into a rule-based chunker. Asked human judges to annotate base noun phrases in naturally occurring text and trained a ML-based system to recognize these phrases. Compared the performance of the two ruleml-based systems. USCand INFORMATION SCIENCES INSTITUTE

5 It pays off to annotate data

6 It matters who annotates the data

7 How can we do well while annotating less data? Active learning Active learning with one classifier Active learning with a committee of classifiers Bootstrapping Bootstrapping with one classifier Bootstrapping with a committee of classifiers Co-training

8 Active learning with one classifier Input: small annotated corpus + large un-annotated corpus. 3. Train classifier on annotated data. 4. Apply classifier on unlabeled examples. 5. Elicit human judgments for examples on which classifier had the lowest confidence. 6. Add new labeled data to the annotated corpus. 7. Retrain classifier and test on held-out data. 8. If improvement, go to 2.

9 Active learning with multiple classifiers Input: small annotated corpus + large un-annotated corpus. 3. Train multiple classifiers on annotated data. 4. Apply classifiers on unlabeled examples. 5. Elicit human judgments for examples on which classifiers agree the least. 6. Add new labeled data to the annotated corpus. 7. Retrain classifier and test on held-out data. 8. If improvement, go to 2.

10 Active learning helps [Banko and Brill, ACL-2001]

11 Active learning helps [Ngai and Yarowsky, NAACL-2000]

12 Active learning worked in all cases that I know of.

13 Bootstrapping with one classifier Input: small annotated corpus + large un-annotated corpus. 3. Train classifier on annotated data. 4. Apply classifier on unlabeled examples. 5. Add to the training corpus the examples that are labeled with high confidence. 6. Retrain classifier (and test on held-out data). 7. If improvement, go to 2.

14 Bootstrapping with multiple classifiers Input: small annotated corpus + large un-annotated corpus. 3. Train classifiers on annotated data. 4. Apply classifiers on unlabeled examples. 5. Add to the training corpus the examples that are given the same label by all (most of) the classifiers. 6. Retrain classifiers (and test on held-out data). 7. If improvement, go to 2.

15 Bootstrapping example [Yarowsky ACL-95] Extract from a corpus all instances of a polysemous word (7538 instances of plant). Sense??????? Training Examples company said the plant is still operating Although thousands of plant and animal species zonal distribution of plant life to strain microscopic plant life from the Nissan car and truck plant in Japan discovered at a St. Louis plant manufacturing automated manufacturing plant in Fremont

16 Start with a simple classifier and create a seed corpus Start with a simple classifier: plant A; manufacturing B 82 examples of living plants (1%) 106 examples of manufacturing plants (1%) 7360 residual examples Sense A A A???? B B B Training Examples zonal distribution of plant life to strain microscopic plant life from the Nissan car and truck plant in Japan company said the plant is still operating Although thousands of plant and animal species discovered at a St. Louis plant manufacturing automated manufacturing plant in Fremont

17 Seed corpus

18 1. Train supervised classifier on seed corpus Collocation Sense plant life A manufacturing plant B life (within 2-10 words) A manufacturing (within 2-10 words) B animal (within 2-10 words) A equipment (within 2-10 words) B

19 2. Apply classifier on entire data

20 Rest of the algorithm 1. Optionally use one-sense-per-discourse filter and augment labeled data 2. Repeat steps 1, 2, 3 iteratively. Evaluation: Baseline: 63.9% Supervised: 96.1% Bootstrapping: 96.5%

21 Bootstrapping does not work in all cases (than vs. then [Banko and Brill, ACL-2001]) Test accuracy % Total training data 106 with labeled seed corpus Seed + 5 x 106 unsupervised Seed unsupervised Seed unsupervised Seed + 5 x 108 unsupervised supervised

22 Co-training [Blum and Mitchell, COLT-1998] My advisor Professor Smith Professor John Smith I teach computer courses and advise students including Mary Kae Bill Blue I work on the following projects: - machine learning for web classification - active learning for NLP - software engineering

23 Co-training Input: L set of labeled training examples U set of unlabeled examples Loop: Learn hyperlink-based classifier H from L. Learn full-text classifier F from L. Allow H to label p positive and n negative examples from U (same distribution as in L). Allow F to label p positive and n negative examples from U. Add these self-labeled examples to L. Why does this work? Examples that are easy to label by classifier X may be hard cases for the classifier Y. Classifier Y may learn something new from the examples labeled by X.

24 Example: Error rates for a web classifier Problem: classify web pages as academic course (yes or no). Data: 16 labeled examples and 800 unlabeled examples taken from one department. Page-based classifier Hyperlink-based classifier Combined classifier (equal votes) Supervised training Co-training

25 Co-training does not work in all cases [Pierce and Cardie, EMNLP-2001] Task: identification of base nouns based on left and right context words.

26 Unsupervised learning Pattern discovery. Language modeling (text as sequence of words) Unsupervised induction of syntactic structure. Unsupervised induction of POS taggers and base noun identifiers for non-english. Hidden variables: the EM algorithm.

27 Language modeling as soon as I would like P(w1, w2, w3,, wn) Useful in Speech recognition Machine translation Summarization/Generation Any application in which we produce text

28 N-grams models P(w1, w2,, wn) = P(w1) P(w2 w1) P(w3 w1, w2) P(w4 w1, w2, w3 ) P(wn w1, w2,, wn-1) P(w1) P(w2 w1) P(w3 w1, w2) P(w4 w2, w3 ) P(wn wn-2, wn-1) Estimation: P(c a, b) = count(a, b, c)/count(a, b) Smoothing when count(a,b) or count(a,b,c) are 0. Still the most popular language model: never underestimate the power of n-grams. Syntax-based language models: [Chelba and Jelinek, ACLUSC1998], INFORMATION SCIENCES INSTITUTE [Charniak, ACL-2001]. [Roark, CL-2001],

29 Unsupervised induction of syntactic structures [van Zaanen ICML-2000] [Harris, 1951] Methods in structural linguistics. University of Chicago Press: Two constituents of the same type can be replaced. IDEA: Find in a corpus parts of sentences that can be replaced and assume that these parts are syntactic constituents. Example: Show me (flights from Atlanta to Boston.) Show me (the rates for flight 1943.) (Book Delta 128) from Dallas to Boston. (Give me all flights) from Dallas to Boston.

30 Algorithm 1. Find overlapping segments in all sentence pairs (string edit distance). Dissimilar parts are considered possible constituents and are assigned unique types (labels: X1, X2 ). 2. When multiple overlaps occur use various selection criteria First learned constituent is good. Constituent that occurs most often is good. 3. Apply steps 1 and 2 recursively.

31 Evaluation ATIS corpus: 716 sentences with 11,777 constituents. Examples: Corpus: What is (the name of (the airport in Boston) NP ) NP Learned: What is the (name of the (airport in Boston) C ) C Corpus: Explain classes ((QW) NP and (QX) NP and (Y) NP) NP Learned: Explain classes QW and (QX and (Y) C ) C Non-crossing bracket precision: Non-crossing bracket recall: Lots of room for improvement: Weakening the exact match. Large scale experiments.

32 Induction of POS taggers and base noun identifiers for non-english languages [Yarowsky and Ngai NAACL2001] For many languages, no NLP analyzers exist. Bottleneck: lack of labeled data. IDEA: use parallel corpora and existing statistical machine translation software/techniques to automatically label non-english texts.

33 Projecting POS tag and base noun-phrase structure across languages

34 Difficulties Statistical MT alignment programs yield relatively low accuracy word alignments. Very often translations are not literal. Mismatch between the annotation needs of two languages (gender in French and English).

35 POS tagger induction Run GIZA ( on parallel corpus of 2M words. Run POS tagger on English text. Automatically induce tags for the French. Train probabilistic noisy-channel tagger on automatically induced French tags. Downweight or exclude from the training data the segments that are likely to be aligned poorly. Train lexical priors P(t w) and tag sequence models P(t2 t1) using aggressive generalization techniques (most words have one possible core tag). Test performance on held-out data and out-of-domain manually annotated data (100k: U. Montreal)

36 Evaluation E-F Aligned French Direct transfer: 76% Standard noisy-channel: 86% Noise-robust noisy-channel: 96% Upperbound (trained on heldout goldstandard): 97% Out-of-domain data: Standard noisy-channel: 82% Noise-robust noisy-channel: 94% Upperbound (trained on heldout goldstandard): 98%

37 NP bracketer induction Tag and bracket English text [Brill,CL-1999; Ramshaw and Marcus, VLC-1999] Induce maximal brackets in French/Chinese. Train transformation-based learning (TBL) bracketer on French/Chinese data. Test performance on small corpus of held out sentences (no French or Chinese NP bracketer exists).

38 Evaluation on 50 French sentences Direct, F-measure: Exact % Acceptable % TBL, F-measure: Exact % Acceptable %

39 Hidden variables: the EM algorithm [Knight, AI Magazine, 1997] 1a. ok-voon ororok sprok. 1b. at-voon bichat dat. 7a. lalok farok ororok lalok sprok izok enemok. 7b. wat jjat bichat wat dat vat eneat. 2a. ok-drubel ok-voon anok plok sprok. 2b. at-drubel at-voon pippat rrat dat. 8a. lalok brok anok plok nok. 8b. iat lat pippat rrat nnat. 3a. erok sprok izok hihok ghirok. 3b. totat dat arrat vat hilat. 9a. wiwok nok izok kantok ok-yurp. 9b. totat nnat quat oloat at-yurp. 4a. ok-voon anok drok brok jok. 4b. at-voon krat pippat sat lat. 10a. lalok mok nok yorok ghirok clok. 10b. wat nnat gat mat bat hilat. 5a. wiwok farok izok stok. 5b. totat jjat quat cat. 11a. lalok nok crrrok hihok yorok zanzanok. 11b. wat nnat arrat mat zanzanat. 6a. lalok sprok izok jok stok. 6b. wat dat krat quat cat. 12a. lalok rarok nok izok hihok mok. 12b. wat nnat forat arrat vat gat.

40 EM (Estimation Maximization) [Dempster, Laird, Rubin; JRST, 1977] EM is good for solving chicken and egg problems. Translation: If we knew the word level alignments in a corpus, we would know to estimate t(f e). If we knew t(f e), we would be able to find the word-level alignments in a corpus. Problem to solve: find the word-level alignments and the translation probabilities given this corpus: 1e: b c 1f: x y P(a,f e) = j=1,m t(fj eaj) e: b 2f: y

41 The EM algorithm [Knight, 1999 SMT Tutorial Book] Step 1: Set parameters uniformly. t(x b ) = ½ t(y b) = ½ t(x c) = ½ t(y c) = ½ Step 2: Compute P(a, f e) for all alignments. bc P(a,f e) = ½ * ½ = ¼ xy b P(a, f e) = 1/2 y bc P(a, f e) = ½ * ½ = 1/4 xy

42 The EM algorithm Step 3: Normalize P(a, f e) values to yield P(a e, f) bc P(a e, f) = ¼ / ( ¼ + ¼) = ½ 1 xy b P(a, f e) = ½ / ½ = y bc P(a, f e) = ¼ / ( ¼ + ¼) = ½ xy Step 4: Collect fractional counts tc(x b ) = ½ tc(y b) = ½ + 1 = 3/2 tc(x c) = ½ tc(y c) = ½

43 The EM algorithm Step 5: Normalize fractional counts to get revised parameter values. t(x b) = ½ / ( 3/2 + ½) = ¼ t(y b) = 3/2 / (3/2 + ½) = ¾ t(x c) = ½ / 1 = ½ t(y c) = ½ / 1 = ½ Repeat step 2: Compute P(a, f e) for all alignments. bc P(a,f e) = ¼ * ½ = 1/8 xy b P(a, f e) = 3/4 y bc P(a, f e) = 3/4 * ½ = 3/8 xy

44 The EM algorithm Repeat step 3: Normalize P(a, f e) values to yield P(a e, f) bc P(a e, f) = 1/8 / (1/8 + 3/8) = ¼ xy b P(a, f e) = 1 y bc P(a, f e) = ¾ xy Repeat step 4: Collect fractional counts tc(x b ) = ¼ tc(y b) = ¾ + 1 = 7/4 tc(x c) = ¾ tc(y c) = ¼

45 The EM algorithm Step 5: Normalize fractional counts to get revised parameter values. t(x b) = 1/8 t(y b) = 7/8 t(x c) = 3/4 t(y c) = 1/4 Repeat steps 2-5 many times: t(x b) = t(y b) = t(x c) = t(y c) = At each step, the EM algorithm improves P(f e) for the whole corpus.

46 EM allows one to make MLE under adverse circumstances [Pedersen, EMNLP-2001 EM Panel] MLE (Maximum Likelihood Estimates) Parameters describe the characteristics of a population. Their values are estimated from samples collected from that population. A MLE is a parameter estimate that is most consistent with the sampled data. It maximizes the likelihood of the data P(X Θ). ΘML = argmax Θ L(X Θ).

47 Trivial example: coin tossing 10 trials: h, t, t, t, h, t, t, h, t, t One parameter: = P(h) The MLE is 3/10. Explanation: Given 10 tosses, how likely it is to get 3 heads? L( ) = C103 3 (1 )7 Take the derivative of the log L( )

48 EM: a more complex example Most often, for multinomial distributions it is not possible to find the MLE using closed form formulas. 1e: b c Parameters: = {t(x b), t(x c), t(y b), t(y c)} 1f: x y L(X ) = P(e f) = a P(a, f e) e: b 2f: y Maximizing L(X ) has no closed form solution in this case. E-step: Find the expected values of the complete data, given the incomplete data and the current parameter estimates (steps 2 and 3) M-step: Compute MLE as usual (steps 4 and 5).

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