Download e-book for iPad: Ambiguity Resolution in Language Learning: Computational and by Hinrich Schütze

By Hinrich Schütze

ISBN-10: 1575860740

ISBN-13: 9781575860749

ISBN-10: 1575860759

ISBN-13: 9781575860756

This quantity is anxious with how ambiguity and ambiguity solution are realized, that's, with the purchase of the several representations of ambiguous linguistic kinds and the data helpful for choosing between them in context. Schütze concentrates on how the purchase of ambiguity is feasible in precept and demonstrates that exact varieties of algorithms and studying architectures (such as unsupervised clustering and neural networks) can be successful on the activity. 3 forms of lexical ambiguity are taken care of: ambiguity in syntactic categorisation, semantic categorisation, and verbal subcategorisation. the quantity provides 3 diversified versions of ambiguity acquisition: Tag house, observe house, and Subcat Learner, and addresses the significance of ambiguity in linguistic illustration and its relevance for linguistic innateness.

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Extra info for Ambiguity Resolution in Language Learning: Computational and Cognitive Models

Sample text

To be sure, children come to know these things and this knowledge is part of the output of the language-acquisition process; but it is not part of the input, not part of the "evidence" for the emerging system, and thus not part of the triggering experience. [... ] It is in this sense that the stimulus is too impoverished to fully determine the emergent analysis. In that case children must arrive at (3a) [the correct analysis for a phenomenon discussed by Lightfoot HS] on some other, presumably nonexperiential basis.

Each occurrence of "to" with tag "TO" was replaced by the new 40 / AMBIGUITY RESOLUTION IN LANGUAGE LEARNING word "to-TO" and each occurrence of "to" with tag "IN" was replaced by the new word "to-IN". A new disambiguated corpus was created by executing this procedure for all word-tag combinations. The induction procedure for natural contexts was then run on this disambiguated corpus. 20,000 natural contexts (without rare words and punctuation marks) were randomly selected. Each context was encoded as a 1,000 dimensional vector (consisting of four context vectors, based on disambiguated words).

This SYNTACTIC CATEGORIZATION / 27 gives rise to lexical ambiguity, which this classification procedure [Finch's technique HS] is unable to directly account for. [... ] An analysis of the dendrogram, however, shows that words which are often assigned different categories form a subcategory of their own. This is especially true of words which are sometime [s] nouns, and sometimes verbs. [... ] Finch's defense addresses only part of the problem. It is not sufficient to identify potentially ambiguous words.

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Ambiguity Resolution in Language Learning: Computational and Cognitive Models by Hinrich Schütze

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