1. Word and Phrase Alignment Presenters: Marta Tatu Mithun Balakrishna

2. Translating Collocations for Bilingual Lexicons: A Statistical Approach Frank Smadja, Kathleen R. McKeown and Vasileios Hatzivassiloglou CL-1996

3. 3 Overview – Champollion Translates collocations from English into French using an aligned corpus (Hansards) The translation is constructed incrementally, adding one word at a time Correlation method: the Dice coefficient Accuracy between 65% and 78%

4. 4 The Similarity Measure Dice coefficient (Dice, 1945) where p(X, Y), p(X), and p(Y) are the joint and marginal probability of X and Y If the probabilities are estimated using maximum likelihood, then where f X, f Y, and f XY are the absolute frequencies of appearance of “1”s for X and Y

5. 5 Algorithm - Preprocessing Source and target language sentences must be aligned (Gale and Church 1991) List of collocations to be translated must be provided (Xtract, Smadja 1993)

6. 6 Algorithm 1/3 1. Champollion identifies a set S of k words highly correlated with the source collocation The target collocation is in the powerset of S These words have a Dice-measure  T d ( = 0.10) and appear  T f ( = 5 ) times 2. Form all pairs of words from S 3. Evaluate the correlation between each pair and the source collocation (Dice)

7. 7 Algorithm 2/3 4. Keep pairs that score above the threshold T d 5. Construct 3–word elements containing one of the highly correlated pairs plus a member of S 6. … 7. Until for some n ≤ k, no n –word scores above the threshold

8. 8 Algorithm 3/3 8. Champollion selects the best translation among the top candidates 9. In case of ties, the longer collocation is preferred 10. Determine whether the selected translation is a single word, a flexible, or a rigid collocation, in case of multiword translations Are the words used consistently in the same order and at the same distance?

9. 9 Experimental Setup DB1 = 3.5*10 6 words (8 months of 1986) DB2 = 8.5*10 6 words (1986 and 1987) C1 = 300 collocations from DB1 of mid- range frequency C2 = 300 collocations from 1987 C3 = 300 collocations from 1988 Three fluent bilingual speakers Canadian French vs. continental French

10. 10 Results

11. 11 Future Work Translating the closed class words Tools for the target language Separating corpus-dependent translations from general ones Handling low frequency collocations Analysis of the effects of thresholds Incorporating the length of the translation into the score Using nonparallel corpora

12. 12 Comments

13. A Pattern Matching Method for Finding Noun and Proper Noun Translations from Noisy Parallel Corpora Pascal Fung ACL-1995

14. 14 Goal of the Paper Create bilingual lexicon of nouns and proper nouns From unaligned, noisy parallel texts of Asian/Indo-European language pairs Pattern matching method

15. 15 Introduction Previous research on sentence-aligned, parallel texts Alignment not always practical Unclear sentence boundaries in corpora Noisy text segments present in only one language Two main steps Find small bilingual primary lexicon Compute a better secondary lexicon from these partially aligned texts

16. 16 Algorithm 1. Tag the English half of the parallel text Nouns and proper nouns (they have consistent translations over the entire text) Tagged English part with a modified POS tagger Find translations for nouns, plural nouns and proper nouns only

17. 17 Algorithm 2. Positional Difference Vectors Correspondence between a word and its translated counterpart In their frequency In their positions Correspondence need not be linear Calculation p – position vector of a word V – positional difference vector V[i-1] = p[i] – p[i-1]

18. 18 Algorithm

19. 19 Algorithm 3. Match pairs of positional difference vectors, giving scores Dynamic Time Warping (Fung & McKeown, 1994) For non-identical vectors Trace correspondence between all points in V1 and V2 No penalty for deletions and insertions Statistical filters

20. 20 Dynamic Time Warping Given V1 and V2, which point in V1 corresponds to which point in V2?

21. 21 Algorithm

22. 22 Algorithm 5. Finding anchor points and eliminating noise Every word pair selected to run DTW Obtain DTW score Obtain DTW path Plot DTW paths of all such word pairs Keep highly reliable points and discard rest Point (i,j) is noise if

23. 23 Algorithm

24. 24 Algorithm 6. Finding low frequency bilingual word pairs Non-linear segment binary vectors V 1 [i] = 1 if word occurs in i th segment Binary vector correlation measure

25. 25 Results

26. 26 Comments

27. Automated Dictionary Extraction for “Knowledge- Free” Example-Based Translation Ralf D. Brown TMIMT-1997

28. 28 Goal of the Paper Extract a bilingual dictionary Using a aligned bilingual corpus Perform tests to compare the performance of PanEBMT using Collins Spanish-English dictionary + WordNet English root/synonym list Various automatically extracted bilingual dictionaries

29. 29 Introduction

30. 30 Extracting Bilingual Dictionary Extracted from corpus using Correspondence table Threshold Schema Correspondence Table Two dimensional array Indexed by source language words Indexed by target language words Cross-product word entries of each sentence pair are incremented

31. 31 Extracting Bilingual Dictionary Similar word orders language pairs biased Threshold setting A step function Unreachably high for co-occurrence < MIN Constant otherwise A sliding scale Start at 1.0 for co-occurrence = 1 Slide smoothly to MIN threshold value

32. 32 Extracting Bilingual Dictionary Filtering Symmetric threshold Asymmetric threshold Any elements of Correspondence table which fail both tests set to zero Non-zero elements added to dictionary

33. 33 Extracting Bilingual Dictionary - Results

34. 34 Extracting Bilingual Dictionary - Errors High-frequency Error-ridden terms Short list high frequency words (all words which appear in at least 20% of source sentences) Short list sentence pairs containing extactly one or two high frequency words Results in 7 of 16 words – Zero error Merge with results from first pass

35. 35 Experimental Setup Manually created tokenization – 47 equivalence classes, 880 words and translations of each word Two test texts 275 UN corpus sentences : in-domain 253 Newswire sentences : out-of-domain

36. 36 Results

37. 37 Comments

38. Extracting Paraphrases from a Parallel Corpus Regina Barzilay and Kathleen R. McKeown ACL-2001

39. 39 Overview Corpus-based unsupervised learning algorithm for paraphrase extraction Lexical paraphrases (single and multi-word) (refuse, say no) Morpho-syntactic paraphrases (king’s son, son of the king) (start to talk, start talking) Phrases which appear in similar contexts are paraphrases

40. 40 Data Multiple English translations of literary texts written by foreign authors Madam Bovary, Fairy Tales, Twenty Thousand Leagues Under the Sea, etc. 11 translations

41. 41 Preprocessing Sentence alignment Translations of the same source contain a number of identical words 42% of the words in corresponding sentences are identical (average) Dynamic programming (Gale & Church, 1991) 94.5% correct alignments (127 sentences) POS tagger and chunker  NP and VP

42. 42 Algorithm – Bootstrapping Co-training method: DLCoTrain (Collins & Singer, 1999) Similar contexts surround two phrases  paraphrase Having good paraphrase predictor contexts  new paraphrases 1. Analyze contexts surrounding identical words in aligned sentence pairs 2. Use these contexts to learn new paraphrases

43. 43 Feature Extraction Paraphrase features Lexical: tokens for each phrase in the paraphrase pair Syntactic: POS tags Contextual features: left and right syntactic contexts surrounding the paraphrase (POS n-grams) tried to comfort her  left 1 =“VB 1 TO 2 ”, right 1 =“PRP$ 3 ” tried to console her  left 2 =“VB 1 TO 2 ”, right 2 =“PRP$ 3 ”

44. 44 Algorithm Initialization Identical words are the seeds (positive paraphrasing examples) Negatives are created by pairing each word with all the other words in the sentence Training of the context classifier Record contexts around positive and negative paraphrases of length ≤ 3 Identify the strong predictors based on their strength and frequency

45. 45 Algorithm Keep the most frequent k = 10 contexts with a strength > 95% Training of the paraphrasing classifier Using the context rules extracted previously, derive new pairs of paraphrases When no more paraphrases are discovered, stop

46. 46 Results 9483 paraphrases, 25 morpho-syntactic rules Out of 500: 86.5% (without context), 91.6% (with context) correct paraphrases 69% recall evaluated on 50 sentences

47. 47 Future Work Extract paraphrases from comparable corpora (news reports about the same event) Improve the context representation

48. 48 Comments

49. 49 Thank You !