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Raciolinguistics

Edited by H. Samy Alim, John R. Rickford, and Arnetha F. Ball

Raciolinguistics "Brings together a critical mass of scholars to form a new field dedicated to theorizing and analyzing language and race together."


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Sociolinguistics from the Periphery

By Sari Pietikäinen, FinlandAlexandra Jaffe, Long BeachHelen Kelly-Holmes, and Nikolas Coupland

Sociolinguistics from the Periphery "presents a fascinating book about change: shifting political, economic and cultural conditions; ephemeral, sometimes even seasonal, multilingualism; and altered imaginaries for minority and indigenous languages and their users."


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.

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