Personalized PageRank over WordNet for Similarity and Word Sense - PowerPoint PPT Presentation

Personalized PageRank over WordNet for Similarity and Word Sense Disambiguation Eneko Agirre e.agirre@ehu.es (joint work with Aitor Soroa, some slides from Enrique Alfonseca) University of the Basque Country (Currently visiting Stanford) Google, 2009 Agirre (UBC) Personalized PageRank over WordNet Google 2009 1 / 40

Introduction Summary Present an integrated software based on Knowledge Bases (e.g. WordNet) for: Similarity of word pairs Disambiguate words with respect to knowledge base concepts (aka Word Sense Disambiguation) Excellent results (EACL, NAACL, IJCAI 2009) Open source: http://ixa2.si.ehu.es/ukb/ Agirre (UBC) Personalized PageRank over WordNet Google 2009 2 / 40

Introduction Outline Introduction 1 WordNet, PageRank and Personalized PageRank 2 PPR for similarity [Agirre et al.2009b] 3 PPR for WSD [Agirre and Soroa2009] 4 PPR and WSD on specific domains [Agirre et al.2009a] 5 Conclusions 6 Agirre (UBC) Personalized PageRank over WordNet Google 2009 3 / 40

Introduction Similarity Measuring semantic similarity and relatedness are well studied problems in lexical semantics: Given two words or multiword-expressions, estimate how similar or related they are. Relatedness is a more general relationship, including topical relatedness or meronymy. Typically implemented as calculating a numeric value of similarity/relatedness. Agirre (UBC) Personalized PageRank over WordNet Google 2009 4 / 40

Introduction Similarity examples RG dataset WordSim353 dataset cord smile 0.02 king cabbage 0.23 rooster voyage 0.04 professor cucumber 0.31 noon string 0.04 ... ... investigation effort 4.59 glass jewel 1.78 smart student 4.62 magician oracle 1.82 ... ... movie star 7.38 cushion pillow 3.84 ... cemetery graveyard 3.88 journey voyage 9.29 automobile car 3.92 midday noon 9.29 midday noon 3.94 fuck sex 9.44 gem jewel 3.94 tiger tiger 10.00 Agirre (UBC) Personalized PageRank over WordNet Google 2009 5 / 40

Introduction Similarity Two main approaches: Knowledge-based (Roget’s Thesaurus, WordNet, etc.) Corpus-based, also known as distributional similarity (co-occurrences) Many potential applications , overcome brittleness (word match), specially in very short texts, information retrieval, textual entailment, machine translation. Agirre (UBC) Personalized PageRank over WordNet Google 2009 6 / 40

Introduction Similarity Two main approaches: Knowledge-based (Roget’s Thesaurus, WordNet, etc.) Corpus-based, also known as distributional similarity (co-occurrences) Many potential applications , overcome brittleness (word match), specially in very short texts, information retrieval, textual entailment, machine translation. Agirre (UBC) Personalized PageRank over WordNet Google 2009 6 / 40

Introduction Word Sense Disambiguation (WSD) Goal: determine the senses of the words in a text. “. . . but the location on the south bank of the Thames estuary.” “. . . cash includes cheque payments, bank transfers . . . ” Dictionary (e.g. WordNet): bank#1 sloping land, especially the slope beside a body of water. bank#2 a financial institution that accepts deposits and. . . bank#3 an arrangement of similar objects in row or in tiers. bank#4 a long ridge or pile. . . . (10 senses total) Many potential applications, enable natural language understanding, link text to knowledge base, deploy semantic web. Agirre (UBC) Personalized PageRank over WordNet Google 2009 7 / 40

Introduction Word Sense Disambiguation (WSD) Goal: determine the senses of the words in a text. “. . . but the location on the south bank of the Thames estuary.” “. . . cash includes cheque payments, bank transfers . . . ” Dictionary (e.g. WordNet): bank#1 sloping land, especially the slope beside a body of water. bank#2 a financial institution that accepts deposits and. . . bank#3 an arrangement of similar objects in row or in tiers. bank#4 a long ridge or pile. . . . (10 senses total) Many potential applications, enable natural language understanding, link text to knowledge base, deploy semantic web. Agirre (UBC) Personalized PageRank over WordNet Google 2009 7 / 40

Introduction Word Sense Disambiguation (WSD) Goal: determine the senses of the words in a text. “. . . but the location on the south bank of the Thames estuary.” “. . . cash includes cheque payments, bank transfers . . . ” Dictionary (e.g. WordNet): bank#1 sloping land, especially the slope beside a body of water. bank#2 a financial institution that accepts deposits and. . . bank#3 an arrangement of similar objects in row or in tiers. bank#4 a long ridge or pile. . . . (10 senses total) Many potential applications, enable natural language understanding, link text to knowledge base, deploy semantic web. Agirre (UBC) Personalized PageRank over WordNet Google 2009 7 / 40

Introduction Word Sense Disambiguation (WSD) Supervised corpus-based WSD performs best Train classifiers on hand-tagged data (typically SemCor) Data sparseness, e.g. bank 48 examples (25,20,2,1,0. . . ) Results decrease when train/test from different sources (even Brown, BNC) Decrease even more when train/test from different domains Knowledge-based WSD Uses information in a KB (WordNet) Performs close to but lower than Most Frequent Sense Vocabulary coverage Relation coverage But . . . Agirre (UBC) Personalized PageRank over WordNet Google 2009 8 / 40

Introduction Word Sense Disambiguation (WSD) Supervised corpus-based WSD performs best Train classifiers on hand-tagged data (typically SemCor) Data sparseness, e.g. bank 48 examples (25,20,2,1,0. . . ) Results decrease when train/test from different sources (even Brown, BNC) Decrease even more when train/test from different domains Knowledge-based WSD Uses information in a KB (WordNet) Performs close to but lower than Most Frequent Sense Vocabulary coverage Relation coverage But . . . Agirre (UBC) Personalized PageRank over WordNet Google 2009 8 / 40

Introduction Domain adaptation Deploying NLP techniques in real applications is challenging, specially for WSD: Sense distributions change across domains Data sparseness hurts more Context overlap is reduced New senses, new terms But. . . Some words get less interpretations in domains: bank in finance, coach in sports Agirre (UBC) Personalized PageRank over WordNet Google 2009 9 / 40

Introduction Domain adaptation Deploying NLP techniques in real applications is challenging, specially for WSD: Sense distributions change across domains Data sparseness hurts more Context overlap is reduced New senses, new terms But. . . Some words get less interpretations in domains: bank in finance, coach in sports Agirre (UBC) Personalized PageRank over WordNet Google 2009 9 / 40

Introduction Similarity and WSD If using knowledge-bases, both WSD and Similarity are closely intertwined: Similarity between words based on similarity between senses (implicitly doing disambiguation) WSD uses similarity of senses to context, or similarity between senses in context Agirre (UBC) Personalized PageRank over WordNet Google 2009 10 / 40

Introduction Outline Introduction 1 WordNet, PageRank and Personalized PageRank 2 PPR for similarity [Agirre et al.2009b] 3 PPR for WSD [Agirre and Soroa2009] 4 PPR and WSD on specific domains [Agirre et al.2009a] 5 Conclusions 6 Agirre (UBC) Personalized PageRank over WordNet Google 2009 11 / 40

WordNet, PageRank and Personalized PageRank Outline Introduction 1 WordNet, PageRank and Personalized PageRank 2 PPR for similarity [Agirre et al.2009b] 3 PPR for WSD [Agirre and Soroa2009] 4 PPR and WSD on specific domains [Agirre et al.2009a] 5 Conclusions 6 Agirre (UBC) Personalized PageRank over WordNet Google 2009 12 / 40

WordNet, PageRank and Personalized PageRank Wordnet Most widely used hierarchically organized lexical database for English (Fellbaum, 1998) Broad coverage of nouns, verbs, adjectives, adverbs Main unit: synset (concept) depository financial institution, bank#2, banking company a financial institution that accepts deposits and. . . Relations between concepts: synonymy (built-in), hyperonymy, antonymy, meronymy, entailment, derivation, gloss Closely linked versions in several languages Agirre (UBC) Personalized PageRank over WordNet Google 2009 13 / 40

WordNet, PageRank and Personalized PageRank Wordnet Example of hypernym relations: bank financial institution, financial organization organization social group group, grouping abstraction, abstract entity entity Representing WordNet as a graph: Nodes represent concepts Edges represent relations (undirected) In addition, directed edges from words to corresponding concepts (senses) Agirre (UBC) Personalized PageRank over WordNet Google 2009 14 / 40

WordNet, PageRank and Personalized PageRank PageRank Given a graph, ranks nodes according to their relative structural importance If an edge from n i to n j exists, a vote from n i to n j is produced Strength depends on the rank of n i The more important n i is, the more strength its votes will have. PageRank can also be viewed as the result of a random walk process Rank of n i represents the probability of a random walk over the graph ending on n i , at a sufficiently large time. Agirre (UBC) Personalized PageRank over WordNet Google 2009 15 / 40