Publishing Partner: Cambridge University Press CUP Extra Publisher Login
amazon logo
More Info

New from Oxford University Press!


May I Quote You on That?

By Stephen Spector

A guide to English grammar and usage for the twenty-first century, pairing grammar rules with interesting and humorous quotations from American popular culture.

New from Cambridge University Press!


The Cambridge Handbook of Endangered Languages

Edited By Peter K. Austin and Julia Sallabank

This book "examines the reasons behind the dramatic loss of linguistic diversity, why it matters, and what can be done to document and support endangered languages."

Academic Paper

Title: Exploiting the Wikipedia structure in local and global classification of taxonomic relations
Author: Quang Xuan Do
Institution: University of Illinois at Urbana-Champaign
Author: Dan Roth
Institution: University of Illinois at Urbana-Champaign
Linguistic Field: Computational Linguistics; Text/Corpus Linguistics
Abstract: Determining whether two terms have an ancestor relation (e.g. Toyota Camry and car) or a sibling relation (e.g. Toyota and Honda) is an essential component of textual inference in Natural Language Processing applications such as Question Answering, Summarization, and Textual Entailment. Significant work has been done on developing knowledge sources that could support these tasks, but these resources usually suffer from low coverage, noise, and are inflexible when dealing with ambiguous and general terms that may not appear in any stationary resource, making their use as general purpose background knowledge resources difficult. In this paper, rather than building a hierarchical structure of concepts and relations, we describe an algorithmic approach that, given two terms, determines the taxonomic relation between them using a machine learning-based approach that makes use of existing resources. Moreover, we develop a global constraint-based inference process that leverages an existing knowledge base to enforce relational constraints among terms and thus improves the classifier predictions. Our experimental evaluation shows that our approach significantly outperforms other systems built upon the existing well-known knowledge sources.


This article appears IN Natural Language Engineering Vol. 18, Issue 2, which you can READ on Cambridge's site .

Add a new paper
Return to Academic Papers main page
Return to Directory of Linguists main page