Publikace
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Citace
2015. : On Continuous Space Word Representations as Input of LSTM Language Model . Statistical Language and Speech Processing,
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Abstrakt
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.
Detail publikace
Název: | On Continuous Space Word Representations as Input of LSTM Language Model |
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Autor: | Soutner D. ; Müller L. |
Jazyk publikace: | česky |
Datum vydání: | 17.11.2015 |
Rok vydání: | 2015 |
Typ publikace: | Stať ve sborníku |
Název knihy: | Statistical Language and Speech Processing |
DOI: | 10.1007/978-3-319-25789-1_25 |
BibTeX
@ARTICLE{SoutnerD_2015_OnContinuousSpace, author = {Soutner D. and M\"{u}ller L.}, title = {On Continuous Space Word Representations as Input of LSTM Language Model}, year = {2015}, booktitle = {Statistical Language and Speech Processing}, doi = {10.1007/978-3-319-25789-1_25}, url = {http://www.kky.zcu.cz/en/publications/SoutnerD_2015_OnContinuousSpace}, }