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The Social Origins of Language

By Daniel Dor

Presents a new theoretical framework for the origins of human language and sets key issues in language evolution in their wider context within biological and cultural evolution


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Preposition Placement in English: A Usage-Based Approach

By Thomas Hoffmann

This is the first study that empirically investigates preposition placement across all clause types. The study compares first-language (British English) and second-language (Kenyan English) data and will therefore appeal to readers interested in world Englishes. Over 100 authentic corpus examples are discussed in the text, which will appeal to those who want to see 'real data'


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Free Access 4 You

Free access to several Brill linguistics journals, such as Journal of Jewish Languages, Language Dynamics and Change, and Brill’s Annual of Afroasiatic Languages and Linguistics.


Academic Paper


Title: A general feature space for automatic verb classification
Author: Eric Joanis
Institution: University of Toronto
Author: Suzanne Stevenson
Institution: University of Toronto
Author: David James
Institution: University of Toronto
Linguistic Field: Computational Linguistics; Semantics
Abstract: Lexical semantic classes of verbs play an important role in structuring complex predicate information in a lexicon, thereby avoiding redundancy and enabling generalizations across semantically similar verbs with respect to their usage. Such classes, however, require many person-years of expert effort to create manually, and methods are needed for automatically assigning verbs to appropriate classes. In this work, we develop and evaluate a feature space to support the automatic assignment of verbs into a well-known lexical semantic classification that is frequently used in natural language processing. The feature space is general – applicable to any class distinctions within the target classification; broad – tapping into a variety of semantic features of the classes; and inexpensive – requiring no more than a POS tagger and chunker. We perform experiments using support vector machines (SVMs) with the proposed feature space, demonstrating a reduction in error rate ranging from 48% to 88% over a chance baseline accuracy, across classification tasks of varying difficulty. In particular, we attain performance comparable to or better than that of feature sets manually selected for the particular tasks. Our results show that the approach is generally applicable, and reduces the need for resource-intensive linguistic analysis for each new classification task. We also perform a wide range of experiments to determine the most informative features in the feature space, finding that simple, easily extractable features suffice for good verb classification performance.

CUP at LINGUIST

This article appears in Natural Language Engineering Vol. 14, Issue 3, which you can read on Cambridge's site or on LINGUIST .



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