A Vector Field Approach to Lexical Semantics

Peter Wittek, Sandor Daranyi, Ying-Hsang Liu

Research output: Book chapter/Published conference paperConference paper

3 Citations (Scopus)
4 Downloads (Pure)

Abstract

We report work in progress on measuring “forces” underlying the semantic drift by comparing it with plate tectonics in geology. Based on a brief survey of energy as a key concept in machine learning, and the Aristotelian concept of potentiality vs. actuality allowing for the study of energy and dynamics in language, we propose a field approach to lexical analysis. Until evidence to the contrary, it was assumed that a classical field in physics is appropriate to model word semantics. The approach used the distributional hypothesis to statistically model word meaning. We do not address the modelling of sentence meaning here. The computability of a vector field for the indexing vocabulary of the Reuters-21578 test collection by an emergent self-organizing map suggests that energy minima as learnables in machine learning presuppose concepts as energy minima in cognition. Our finding needs to be confirmed by a systematic evaluation.
Original languageEnglish
Title of host publicationQuantum Interaction
Subtitle of host publication8th International Conference, QI 2014
Place of PublicationGermany
PublisherSpringer
Pages78-89
Number of pages12
Volume8951
ISBN (Electronic)9783319159300
DOIs
Publication statusPublished - 2015
Event8th International Conference on Quantum Interaction: QI 2014 - Conference Center Lihn, Filzbach, Switzerland
Duration: 30 Jun 201403 Jul 2014
http://www.mindmatter.de/QI2014/index.html

Publication series

Name
ISSN (Print)0302-9743

Conference

Conference8th International Conference on Quantum Interaction
CountrySwitzerland
CityFilzbach
Period30/06/1403/07/14
Internet address

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    Wittek, P., Daranyi, S., & Liu, Y-H. (2015). A Vector Field Approach to Lexical Semantics. In Quantum Interaction: 8th International Conference, QI 2014 (Vol. 8951, pp. 78-89). Springer. https://doi.org/10.1007/978-3-319-15931-7_7