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CNR ExploRA
Part of book or chapter of book . 2018
Data sources: CNR ExploRA
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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Part of book or chapter of book . 2018
Data sources: IRIS Cnr
https://doi.org/10.1093/oxford...
Part of book or chapter of book . 2018 . Peer-reviewed
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Morphological Theory and Computational Linguistics

Authors: Pirrelli; Vito;

Morphological Theory and Computational Linguistics

Abstract

The chapter provides a computer-based, algorithmic view of issues of lexical processing, ranging from the encoding of input data to the structure of output representations, going through the basic operations of word splitting, storage, access, retrieval, and assembly of intermediate representations. By illustrating the contribution of different computational frameworks (such as finite state automata, hierarchical lexica, artificial neural networks, and statistical language models) to our understanding of aspects of lexical organization, the chapter discusses the implications of theoretical models of morphology for computational models of word processing, as well as the implications of computer models for theoretical issues. In this perspective, much of current work in computational morphology does not only provide a challenging test bed for box and arrow models of lexical knowledge, but it also promises to bridge the persisting gap between theoretical frameworks and behaviourally oriented research in lexical modelling.

Keywords

lexical modelling, word processing, finite state technology, word storage, computational morphology, artificial neural networks, machine language learning

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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