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Vector-based semantic analysis: representing word meanings based on random labels
Number of Authors: 1
2001 (English)Conference paper, (Refereed)
Abstract [en]

Vector-based semantic analysis is the practice of representing word meanings as semantic vectors, calculated from the co-occurrence statistics of words in large text data. This paper discusses the theoretical presumptions behind this practice, and a representational scheme based on the Distributional Hypothesis is identified as the rationale for vector-based semantic analysis. A new method for calculating semantic word vectors is then described. The method uses random labelling of words in narrow context windows to calculate semantic context vectors for each word type in the text data. The method is evaluated with a standardised synonym test, and it is shown that incorporating linguistic information in the context vectors can enhance the results.

Place, publisher, year, edition, pages
2001, 1.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:ri:diva-22615OAI: oai:DiVA.org:ri-22615DiVA: diva2:1042180
Conference
Semantic Knowledge Acquisition and Categorisation Workshop at ESSLLI XIII (European Summer School in Logic, Language and Information)
Available from: 2016-10-31 Created: 2016-10-31Bibliographically approved

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Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • 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
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