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Unsupervised Graph-based Word Sense Disambiguation

Using Measures of Word Semantic Similarity

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Bibliography

● Ravi Sinha and Rada Mihalcea, Unsupervised Graph-based Word Sense Disambiguation Using Measures of Word Semantic Similarity, In Proceedings of the IEEE International Conference on Semantic Computing (ICSC 2007), Irvine, CA, September 2007

● Rada Mihalcea, Unsupervised Large-Vocabulary Word Sense Disambiguation with Graph-based Algorithms for Sequence Data Labeling, In Proceedings of the Joint Conference on Human Language Technology / Empirical Methods in Natural Language Processing (HLT/EMNLP), Vancouver, October, 2005

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Plan

1. Introduction2. Graph-based Centrality for WSD3. Measures of Semantic Similarity4. Graph-based Centrality Algorithms5. Demo

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1. Introduction

● WSD = assign automatically the most appropriate meaning to a polysemous word within a given context ● Example: 1. The plant is producing far too little to sustain its operation for more than a year. (fabrică) 2. An overabundance of oxygen was produced by the plant in the third week of the study. (plantă)

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2. Graph-based Centrality for WSD(I)

● GWSD = graph representation used to model word sense dependencies in text (WSD with graphs, not just word window)● Goal: identify the most probable sense (label) for each word

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2. Graph-based Centrality for WSD(II)

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ExampleThe church bells no longer rung on Sundays.● church

1: one of the groups of Christians who have their own beliefs and forms of worship

2: a place for public (especially Christian) worship3: a service conducted in a church

● bell1: a hollow device made of metal that makes a ringing sound when struck2: a push button at an outer door that gives a ringing or buzzing signal when

pushed3: the sound of a bell

● ring1: make a ringing sound2: ring or echo with sound3: make (bells) ring, often for the purposes of musical edification

● Sunday1: first day of the week; observed as a day of rest and worship

by most Christians

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3.Measures of Semantic Similarity● Quantify the degree to which two words are semantically relatedusing information drawn from semantic networks● Word similarity measures

1. Leacock & Chodorow

2. Leck

3. Wu and Palmer

4. Resnik

5. Lin

6. Jiang & Conrath

●

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4.Graph-based Centrality Algorithms

● Indegree

● Closeness

● Betweenness

● Page Rank

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5. Demo - GWSD

●Dependencies● WordNet (semantic hierarchy)● WordNet::QueryData● WordNet::Similarity(implementation of similarity measures)

●Input● Senseval-2, Senseval-3 datasets in Semcor format

●GWSD improvements● Combine similarity measures(jcn for nouns, lch for verbs,

lesk for other parts of speech)● Voting system between 4 centrality algorithms