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Align, Disambiguate, and Walk A Unified Approach for Measuring Semantic Similarity

Align, Disambiguate, and Walk A Unified Approach for Measuring Semantic Similarity. Mohammad Taher Pilehvar David Jurgens Roberto Navigli. Semantic Similarity ; how similar are a pair of lexical items ?. Semantic Similarity. Sense Level. Sentence Level. Word Level.

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Align, Disambiguate, and Walk A Unified Approach for Measuring Semantic Similarity

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  1. Align, Disambiguate, and WalkA Unified Approach for Measuring Semantic Similarity Mohammad TaherPilehvar David Jurgens Roberto Navigli

  2. SemanticSimilarity; howsimilar are a pair of lexicalitems?

  3. Semantic Similarity Sense Level Sentence Level Word Level

  4. Semantic Similarity Sense Level Sentence Level Word Level

  5. Semantic Similarity Sentence level • Applications • Paraphraserecognition (Tsatsaronis et al., 2010) • MT evaluation (Kauchak and Barzilay, 2006) • QuestionAnswering (Surdeanu et al., 2011) • TextualEntailment (Dagan et al., 2006) The worker was terminated The boss fired him

  6. Semantic Similarity Sense Level Sentence Level Word Level

  7. SemanticSimilarity Word level • Applications • Lexicalsimplification (Biran et al., 2011) Locuacious → Talkative • Lexicalsubstitution (McCarthy and Navigli, 2009) • heater fireplace

  8. Semantic Similarity Sense Level Sentence Level Word Level

  9. SemanticSimilarity Sense level firesense #1 • Applications • Coarseningsenseinventories(Snow et al., 2007) • Semanticpriming (Neely et al., 1989) firesense #8

  10. Exisiting Similarity Measures Allison and Dix (1986) Gusfield (1997) Wise (1996) Patwardan(2003) Keselj et al. (2003) Banerjee and Pederson (2003) Salton and McGill (1983) Hirst and St-Onge (1998) Gabrilovich and Markovitch (2007) Lin (1998) Radinsky et al. (2011) Jiang and Conrath (1997) Ramage et al. (2009) Resnik (1995) Yeh et al., (2009) Sussna (1993, 1997) Turney (2007) Wu and Palmer (1994) Landauer et al. (1998) Leacock and Chodorow (1998) Sentence Word Sense

  11. Exisiting Similarity Measures But Allison and Dix (1986) Gusfield (1997) None directlycoversalllevels atthe sametime Wise (1996) Patwardan(2003) Keselj et al. (2003) Banerjee and Pederson (2003) Salton and McGill (1983) Hirst and St-Onge (1998) Gabrilovich and Markovitch (2007) Lin (1998) Different output scales Radinsky et al. (2011) Jiang and Conrath (1997) Ramage et al. (2009) Resnik (1995) Yeh et al., (2009) Sussna (1993, 1997) Different internal representations which are not comparable to each other Turney (2007) Wu and Palmer (1994) Landauer et al. (1998) Leacock and Chodorow (1998) Sense Word Sentence

  12. Contribution Sense Word sentence A unified representation for anylexical item State-of-the-art performance in eachlevel Using onlyWordNet

  13. Advantage 1Unified representation sense sentence text word word sense Alllexicalitems this way

  14. Advantage 2Cross-level semantic similarity sense word sentence A large and imposinghouseMansion Residence#3

  15. Advantage 3Sense-level operation sense word set of senses Ambiguity sentence sense Worker was fired. He was terminated. worker#1 fire#4 terminate#4

  16. Outline sense word text Introduction Methodology Experiments

  17. How Does it work? lexical item 1 lexical item 2 semantic signature 2 semantic signature 1

  18. Semantic Signature sense word sentence Unifiedsemanticsignature

  19. Semantic Signature sense word sentence Alignment-baseddisambiguation Sense or set of senses Unifiedsemanticsignature

  20. Semantic Signature A woman isfryingfood

  21. Semantic Signature Distributional representation over allsynsets in WordNet . . . Importance of thissynset (syn_4) for ourlexical item

  22. Semantic Signature a woman isfryingfood oil#4 ship#1 sugar#2 table#3 physics#1 carpet#2 kitchen#3 cooking#1 natural_gas#2 frying_pan#1

  23. PersonalizedPageRank

  24. PersonalizedPageRank

  25. PersonalizedPageRank a woman isfryingfood { , , } 1 2 1 woman fry food n v n 2 fry v 1 woman 1 n food n

  26. 1 food n 2 food n 3 cook v 1 beverage 2 dish n n 1 fat 1 cooking n 1 french_fries n Theseweightsform a semanticsignature n 1 nutriment n 2 fry v

  27. Comparing Semantic Signatures

  28. Comparing Semantic Signatures • Parametric • Cosine • Non-parametric • WeightedOverlap • Top-k Jaccard

  29. Comparing Semantic SignaturesWeighted Overlap

  30. Comparing Semantic SignaturesWeighted Overlap

  31. Comparing Semantic SignaturesTop-Jaccard

  32. Comparing Semantic SignaturesTop-Jaccard

  33. Alignment-based disambiguation sense word sentence Alignment-baseddisambiguation Sense or set of senses Unifiedsemanticsignature

  34. Alignment-based disambiguation sense word sentence sentence Alignment-baseddisambiguation Sense or set of senses Unifiedsemanticsignature

  35. Whyisdisambiguationneeded? The worker was fired He wasterminated

  36. Alignment-based disambiguation An employeewasterminated from work by his boss. A manager fired the worker.

  37. Alignment-based disambiguation An employeewasterminatedfrom workby hisboss. A managerfiredthe worker.

  38. Alignment-based disambiguation employeen bossn managern firev workn workern terminatev

  39. Alignment-based disambiguation employeen bossn managern firev workn workern terminatev 1 1 1 1 1 1 1 manager boss worker fire work terminate employee n n n v n v n 2 2 2 2 manager 2 boss fire 2 worker work terminate n n n v n n v 3 3 work fire 3 terminate n v . . . . . . v 4 terminate . . . v . . . Sentence 1 Sentence 2

  40. Alignment-based disambiguation employeen bossn managern firev workn workern terminatev 1 1 1 1 1 1 1 manager boss worker fire work terminate employee n n n v n v n 2 2 2 2 manager 2 boss fire 2 worker work terminate n n n v n n v 3 3 work fire 3 terminate n v . . . . . . v 4 terminate . . . v . . . Sentence 1 Sentence 2

  41. Alignment-based disambiguation 0.5 0.3 1 1 1 1 boss work terminate employee n n v n 2 2 boss 2 work terminate 1 manager n n v n 3 work 3 terminate n . . . v 4 terminate v 2 manager . . . n n Tversky (1977) Markman and Gentner (1993)

  42. Alignment-based disambiguation 0.5 0.3 0.3 1 1 1 1 boss work terminate employee n n v n 2 2 boss 2 work terminate 1 manager n n v n 3 work 3 terminate n . . . v 4 terminate v 2 manager . . . n n

  43. Alignment-based disambiguation employeen bossn managern firev workn workern terminatev 1 1 1 1 1 1 1 manager boss worker fire work terminate employee n n n v n v n 2 2 2 2 manager 2 boss fire 2 worker work terminate n n n v n n v 3 3 work fire 3 terminate n v . . . . . . v 4 fire 4 terminate v v . . . Sentence 1 Sentence 2 . . .

  44. Alignment-based disambiguation employeen bossn managern firev workn workern terminatev 1 1 1 1 1 1 1 manager boss worker fire work terminate employee n n n v n v n 2 2 2 2 manager 2 boss fire 2 worker work terminate n n n v n n v 3 3 work fire 3 terminate n v . . . . . . v 4 fire 4 terminate v v . . . Sentence 1 Sentence 2 . . .

  45. Outline sense word text Introduction Methodology Experiments

  46. Experiments • Sentencelevel • SemanticTextualSimilarity (SemEval-2012)

  47. Experiments • Sentencelevel • SemanticTextualSimilarity (SemEval-2012) • Word level • Synonymyrecognition (TOEFL dataset) • Correlation-based (RG-65 dataset)

  48. Experiments • Sentencelevel • SemanticTextualSimilarity (SemEval-2012) • Word level • Synonymyrecognition (TOEFL dataset) • Correlation-based (RG-65 dataset) • Senselevel • CoarseningWordNetsenseinventory

  49. Experiment 1Similarity at Sentence level • Semantic Textual Similarity (STS-12) • 5 datasets • Three evaluation measures • ALL, ALLnrm, and Mean • Top-ranking systems • UKP2 (Bär et al., 2012) • TLSim and TLSyn (Šarić et al., 2012)

  50. Experiment 1Similarity at Sentence level Features • Main features • Cosine • Weighted Overlap • Top-k Jaccard • String-based features • Longest common substring • Longest common subsequence • Greedy string tiling • Character/word n-grams

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