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


New from Oxford University Press!

ad

Evolutionary Syntax

By Ljiljana Progovac

This book "outlines novel and testable hypotheses, contains extensive examples from many different languages" and is "presented in accessible language, with more technical discussion in footnotes."


New from Cambridge University Press!

ad

The Making of Vernacular Singapore English

By Zhiming Bao

This book "proposes a new theory of contact-induced grammatical restructuring" and "offers a new analytical approach to New English from a formal or structural perspective."


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 .



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