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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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The Indo-European Controversy: Facts and Fallacies in Historical Linguistics

By Asya Pereltsvaig and Martin W. Lewis

This book "asserts that the origin and spread of languages must be examined primarily through the time-tested techniques of linguistic analysis, rather than those of evolutionary biology" and "defends traditional practices in historical linguistics while remaining open to new techniques, including computational methods" and "will appeal to readers interested in world history and world geography."


Academic Paper


Title: Inductive probabilistic taxonomy learning using singular value decomposition
Author: Francesca Fallucchi
Institution: Università degli Studi di Roma Tor Vergata
Author: Fabio Massimo Zanzotto
Institution: Università degli Studi di Roma - La Sapienza
Linguistic Field: Computational Linguistics
Abstract: Capturing word meaning is one of the challenges of natural language processing (NLP). Formal models of meaning, such as networks of words or concepts, are knowledge repositories used in a variety of applications. To be effectively used, these networks have to be large or, at least, adapted to specific domains. Learning word meaning from texts is then an active area of research. Lexico-syntactic pattern methods are one of the possible solutions. Yet, these models do not use structural properties of target semantic relations, e.g. transitivity, during learning. In this paper, we propose a novel lexico-syntactic pattern probabilistic method for learning taxonomies that explicitly models transitivity and naturally exploits vector space model techniques for reducing space dimensions. We define two probabilistic models: the direct probabilistic model and the induced probabilistic model. The first is directly estimated on observations over text collections. The second uses transitivity on the direct probabilistic model to induce probabilities of derived events. Within our probabilistic model, we also propose a novel way of using singular value decomposition as unsupervised method for feature selection in estimating direct probabilities. We empirically show that the induced probabilistic taxonomy learning model outperforms state-of-the-art probabilistic models and our unsupervised feature selection method improves performance.

CUP AT LINGUIST

This article appears IN Natural Language Engineering Vol. 17, Issue 1, which you can READ on Cambridge's site or on LINGUIST .



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