Full metadata record
| DC pole | Hodnota | Jazyk |
|---|---|---|
| dc.contributor.author | Soutner, Daniel | |
| dc.contributor.author | Müller, Luděk | |
| dc.date.accessioned | 2017-05-30T07:16:41Z | |
| dc.date.available | 2017-05-30T07:16:41Z | |
| dc.date.issued | 2015 | |
| dc.identifier.citation | SOUTNER, Daniel; MÜLLER, Luděk. On continuous space word representations as input of LSTM language model. In: Statistical Language and Speech Processing. Berlin: Springer, 2015, p. 267-274. (Lectures notes in computer science; 9449). ISBN 978-3-319-25788-4. | en |
| dc.identifier.isbn | 978-3-319-25788-4 | |
| dc.identifier.issn | 0302-9743 | |
| dc.identifier.uri | http://hdl.handle.net/11025/26011 | |
| dc.format | 8 s. | cs |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en | en |
| dc.publisher | Springer | cs |
| dc.relation.ispartofseries | Lectures notes in computer science; 9449 | en |
| dc.rights | © Springer | en |
| dc.subject | umělé neuronové sítě | cs |
| dc.subject | modelování | cs |
| dc.subject | kontinuální reprezentace slov | cs |
| dc.title | On continuous space word representations as input of LSTM language model | en |
| dc.type | článek | cs |
| dc.type | article | en |
| dc.rights.access | openAccess | en |
| dc.type.version | publishedVersion | en |
| dc.description.abstract-translated | Artificial neural networks have become the state-of-the-art in the task of language modelling whereas Long-Short Term Memory (LSTM) networks seem to be an efficient architecture. The continuous skip-gram and the continuous bag of words (CBOW) are algorithms for learning quality distributed vector representations that are able to capture a large number of syntactic and semantic word relationships. In this paper, we carried out experiments with a combination of these powerful models: the continuous representations of words trained with skip-gram/CBOW/GloVe method, word cache expressed as a vector using latent Dirichlet allocation (LDA). These all are used on the input of LSTM network instead of 1-of-N coding traditionally used in language models. The proposed models are tested on Penn Treebank and MALACH corpus. | en |
| dc.subject.translated | artificial neural networks | en |
| dc.subject.translated | modeling | en |
| dc.subject.translated | continuous representations of words | en |
| dc.identifier.doi | 10.1007/978-3-319-25789-1_25 | |
| dc.type.status | Peer-reviewed | en |
| Vyskytuje se v kolekcích: | Články / Articles (KKY) | |
Soubory připojené k záznamu:
| Soubor | Popis | Velikost | Formát | |
|---|---|---|---|---|
| Soutner.pdf | Plný text | 299,81 kB | Adobe PDF | Zobrazit/otevřít |
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