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Real-valued syntactic word vectors
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology. (Computational linguistics)
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology. (Computational linguistics)
2019 (English)In: Journal of experimental and theoretical artificial intelligence (Print), ISSN 0952-813X, E-ISSN 1362-3079Article in journal (Refereed) Published
Abstract [en]

We introduce a word embedding method that generates a set of real-valued word vectors from a distributional semantic space. The semantic space is built with a set of context units (words) which are selected by an entropy-based feature selection approach with respect to the certainty involved in their contextual environments. We show that the most predictive context of a target word is its preceding word. An adaptive transformation function is also introduced that reshapes the data distribution to make it suitable for dimensionality reduction techniques. The final low-dimensional word vectors are formed by the singular vectors of a matrix of transformed data. We show that the resulting word vectors are as good as other sets of word vectors generated with popular word embedding methods.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Word embeddings, context selection, transformation, dependency parsing, singular value decomposition, entropy
National Category
Languages and Literature General Language Studies and Linguistics Computer Systems
Identifiers
URN: urn:nbn:se:uu:diva-392095DOI: 10.1080/0952813X.2019.1653385OAI: oai:DiVA.org:uu-392095DiVA, id: diva2:1346760
Available from: 2019-08-29 Created: 2019-08-29 Last updated: 2019-08-29Bibliographically approved

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Basirat, AliNivre, Joakim

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf