Title: | On continuous space word representations as input of LSTM language model |
Authors: | Soutner, Daniel Müller, Luděk |
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. |
Issue Date: | 2015 |
Publisher: | Springer |
Document type: | článek article |
URI: | http://hdl.handle.net/11025/26011 |
ISBN: | 978-3-319-25788-4 |
ISSN: | 0302-9743 |
Keywords: | umělé neuronové sítě;modelování;kontinuální reprezentace slov |
Keywords in different language: | artificial neural networks;modeling;continuous representations of words |
Abstract in different language: | 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. |
Rights: | © Springer |
Appears in Collections: | Články / Articles (KKY) |
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Soutner.pdf | Plný text | 299,81 kB | Adobe PDF | View/Open |
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http://hdl.handle.net/11025/26011
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