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


New from Oxford University Press!

ad

Oxford Handbook of Corpus Phonology

Edited by Jacques Durand, Ulrike Gut, and Gjert Kristoffersen

Offers the first detailed examination of corpus phonology and serves as a practical guide for researchers interested in compiling or using phonological corpora


New from Cambridge University Press!

ad

The Languages of the Jews: A Sociolinguistic History

By Bernard Spolsky

A vivid commentary on Jewish survival and Jewish speech communities that will be enjoyed by the general reader, and is essential reading for students and researchers interested in the study of Middle Eastern languages, Jewish studies, and sociolinguistics.


New from Brill!

ad

Indo-European Linguistics

New Open Access journal on Indo-European Linguistics is now available!


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.

CUP at LINGUIST

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



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