Learning graph walk based similarity measures for parsed text

Einat Minkov, William W. Cohen

Research output: Contribution to conferencePaperpeer-review

Abstract

We consider a parsed text corpus as an instance of a labelled directed graph, where nodes represent words and weighted directed edges represent the syntactic relations between them. We show that graph walks, combined with existing techniques of supervised learning, can be used to derive a task-specific word similarity measure in this graph. We also propose a new path-constrained graph walk method, in which the graph walk process is guided by high-level knowledge about meaningful edge sequences (paths). Empirical evaluation on the task of named entity coordinate term extraction shows that this framework is preferable to vector-based models for small-sized corpora. It is also shown that the path-constrained graph walk algorithm yields both performance and scalability gains.

Original languageEnglish
Pages907-916
Number of pages10
DOIs
StatePublished - 2008
Externally publishedYes
Event2008 Conference on Empirical Methods in Natural Language Processing, EMNLP 2008, Co-located with AMTA 2008 and the International Workshop on Spoken Language Translation - Honolulu, HI, United States
Duration: 25 Oct 200827 Oct 2008

Conference

Conference2008 Conference on Empirical Methods in Natural Language Processing, EMNLP 2008, Co-located with AMTA 2008 and the International Workshop on Spoken Language Translation
Country/TerritoryUnited States
CityHonolulu, HI
Period25/10/0827/10/08

ASJC Scopus subject areas

  • Information Systems
  • Computational Theory and Mathematics
  • Computer Science Applications

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