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AIC Seminar Series

Lexical Semantic Relatedness with Random Graph Walks

Daniel RamageStanford University[Home Page]

Notice:  hosted by Eric Yeh

Date:  2008-05-13 at 16:00

Location:  EJ228 (SRI E building)  (Directions)

   Abstract

Many systems for tasks such as question answering, multi-document summarization, and information retrieval need robust numerical measures of lexical relatedness. Standard thesaurus-based measures of word pair similarity are based on only a single path between those words in the thesaurus graph. By contrast, we propose a new model of lexical semantic relatedness that incorporates information from every explicit or implicit path connecting the two words in the entire graph. Our model uses a random walk over nodes and edges derived from WordNet links and corpus statistics. We treat the graph as a Markov chain and compute a word-specific stationary distribution via a generalized PageRank algorithm. Semantic relatedness of a word pair is scored by a novel divergence measure, ZKL, that outperforms existing measures on certain classes of distributions. In our experiments, the resulting relatedness measure is the WordNet-based measure most highly correlated with human similarity judgments by rank ordering at rho=.90.

   Bio for Daniel Ramage

Daniel Ramage is a Computer Science PhD candidate at Stanford University working with Prof Christopher Manning in the Natural Language Processing group. He’s interested in the intersection of machine learning, NLP, and Information Retrieval. He’s applied IR techniques to NLP (random graph walks on Wordnet for lexical semantic relatedness) and is currently exploring some applications of NLP techniques to IR (an LDA-like model for clustering web documents that jointly considers page text and del.icio.us tags).

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