Sequence Encoders Enable Large‐Scale Lexical Modeling: Reply to Bowers and Davis (2009)
Sibley, Kello, Plaut, and Elman (2008) proposed the sequence encoder as a model that learns fixed‐width distributed representations of variable‐length sequences. In doing so, the sequence encoder overcomes problems that have restricted models of word reading and recognition to processing only monosy...
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| Veröffentlicht in: | Cognitive science 2009-09, Vol.33 (7), p.1187-1191 |
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| container_title | Cognitive science |
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| creator | Sibley, Daragh E. Kello, Christopher T. Plaut, David C. Elman, Jeffrey L. |
| description | Sibley, Kello, Plaut, and Elman (2008) proposed the sequence encoder as a model that learns fixed‐width distributed representations of variable‐length sequences. In doing so, the sequence encoder overcomes problems that have restricted models of word reading and recognition to processing only monosyllabic words. Bowers and Davis (2009) recently claimed that the sequence encoder does not actually overcome the relevant problems, and hence it is not a useful component of large‐scale word‐reading models. In this reply, it is noted that the sequence encoder has facilitated the creation of large‐scale word‐reading models. The reasons for this success are explained and stand as counterarguments to claims made by Bowers and Davis. |
| doi_str_mv | 10.1111/j.1551-6709.2009.01064.x |
| format | Article |
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Psychophysiology</topic><topic>Recognition</topic><topic>Sequence encoder</topic><topic>Wordforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sibley, Daragh E.</creatorcontrib><creatorcontrib>Kello, Christopher T.</creatorcontrib><creatorcontrib>Plaut, David C.</creatorcontrib><creatorcontrib>Elman, Jeffrey L.</creatorcontrib><collection>Pascal-Francis</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Neurosciences Abstracts</collection><collection>MEDLINE - Academic</collection><collection>Linguistics and Language Behavior Abstracts (LLBA)</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Cognitive science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sibley, Daragh E.</au><au>Kello, Christopher T.</au><au>Plaut, David C.</au><au>Elman, Jeffrey L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sequence Encoders Enable Large‐Scale Lexical Modeling: Reply to Bowers and Davis (2009)</atitle><jtitle>Cognitive science</jtitle><addtitle>Cogn Sci</addtitle><date>2009-09</date><risdate>2009</risdate><volume>33</volume><issue>7</issue><spage>1187</spage><epage>1191</epage><pages>1187-1191</pages><issn>0364-0213</issn><eissn>1551-6709</eissn><coden>COGSD5</coden><abstract>Sibley, Kello, Plaut, and Elman (2008) proposed the sequence encoder as a model that learns fixed‐width distributed representations of variable‐length sequences. 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| ispartof | Cognitive science, 2009-09, Vol.33 (7), p.1187-1191 |
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| source | Wiley Online Library Journals Frontfile Complete; Wiley Online Library Free Content; Education Source (EBSCOhost); EZB-FREE-00999 freely available EZB journals |
| subjects | Biological and medical sciences Fundamental and applied biological sciences. Psychology Information processing Language Large‐scale modeling Orthography Phonetics Phonology Production and perception of written language Psychology. Psychoanalysis. Psychiatry Psychology. Psychophysiology Recognition Sequence encoder Wordforms |
| title | Sequence Encoders Enable Large‐Scale Lexical Modeling: Reply to Bowers and Davis (2009) |
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