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Words in Time and Place: Exploring Language Through the Historical Thesaurus of the Oxford English Dictionary

By David Crystal

Offers a unique view of the English language and its development, and includes witty commentary and anecdotes along the way.


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Thesaurus of English Words and Phrases

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Ampersand: An International Journal of General and Applied Linguistics

Edited By R. Cann, H. Pichler, K. Van De Poel, D. van Olmen, and K. Watson


Academic Paper


Title: A machine learning approach to textual entailment recognition
Author: Fabio Massimo Zanzotto
Institution: Università degli Studi di Roma - La Sapienza
Author: Marco Pennacchiotti
Institution: Yahoo! Research
Author: Alessandro Moschitti
Institution: Università degli Studi di Trento
Linguistic Field: Computational Linguistics; Pragmatics; Semantics; Syntax
Abstract: Designing models for learning textual entailment recognizers from annotated examples is not an easy task, as it requires modeling the semantic relations and interactions involved between two pairs of text fragments. In this paper, we approach the problem by first introducing the class of pair feature spaces, which allow supervised machine learning algorithms to derive first-order rewrite rules from annotated examples. In particular, we propose syntactic and shallow semantic feature spaces, and compare them to standard ones. Extensive experiments demonstrate that our proposed spaces learn first-order derivations, while standard ones are not expressive enough to do so.

CUP at LINGUIST

This article appears in Natural Language Engineering Vol. 15, Issue 4, which you can read on Cambridge's site or on LINGUIST .



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