Modelling word meaning using efficient tensor representations
Symonds, Michael, Bruza, Peter D., Sitbon, Laurianne, & Turner, Ian (2011) Modelling word meaning using efficient tensor representations. In Proceedings of 25th Pacific Asia Conference on Language, Information and Computation, Nanyang Technological University, Singapore.
Models of word meaning, built from a corpus of text, have demonstrated success in emulating human performance on a number of cognitive tasks. Many of these models use geometric representations of words to store semantic associations between words. Often word order information is not captured in these models. The lack of structural information used by these models has been raised as a weakness when performing cognitive tasks.
This paper presents an efficient tensor based approach to modelling word meaning that builds on recent attempts to encode word order information, while providing flexible methods for extracting task specific semantic information.
Impact and interest:
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Keywords:||Semantic Space, Tensors, unsupervised learning, linguistics, tensor encoding|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > COMPUTATION THEORY AND MATHEMATICS (080200)|
Australian and New Zealand Standard Research Classification > PSYCHOLOGY AND COGNITIVE SCIENCES (170000) > COGNITIVE SCIENCE (170200) > Cognitive Science not elsewhere classified (170299)
|Divisions:||Past > Schools > Computer Science|
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Information Systems
Past > Schools > Mathematical Sciences
|Copyright Owner:||Copyright 2011 Mike Symonds, Peter Bruza, Laurianne Sitbon, and Ian Turner|
|Deposited On:||13 Oct 2011 09:50|
|Last Modified:||07 Oct 2014 22:19|
Repository Staff Only: item control page