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A tensor encoding model for semantic processing

Authors:

Michael Symonds,

Peter D. Bruza,

Laurianne Sitbon,

Ian TurnerAuthors Info & Claims

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Pages 2267 - 2270

https://doi.org/10.1145/2396761.2398617

Published: 29 October 2012 Publication History

Abstract

This paper develops and evaluates an enhanced corpus based approach for semantic processing. Corpus based models that build representations of words directly from text do not require pre-existing linguistic knowledge, and have demonstrated psychologically relevant performance on a number of cognitive tasks. However, they have been criticised in the past for not incorporating sufficient structural information. Using ideas underpinning recent attempts to overcome this weakness, we develop an enhanced tensor encoding model to build representations of word meaning for semantic processing. Our enhanced model demonstrates superior performance when compared to a robust baseline model on a number of semantic processing tasks.

References

[1]

J. A. Bullinaria and J. P. Levy. Extracting semantic representations from word co-occurrence statistics: A computational study. Behavior Research Methods, 39:510--526, 2007.

[2]

T. Friedman, R. Casonato, and D. Logan. Innovation forces that will change the nature of information infrastructure. Gartner Research, 2009.

[3]

M. N. Jones and D. J. K. Mewhort. Representing word meaning and order information in a composite holographic lexicon. Psychological Review, 114:1--37, 2007.

[4]

T. K. Landauer and S. T. Dumais. A solution to plato's problem: The latent semantic analysis theory of the acquisition, induction, and representation of knowledge. Psychological Review, 104:211--240, 1997.

[5]

K. Lund and C. Burgess. Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior research methods, instruments and computers, 28:203--208, 1996.

[6]

S. Padó and M. Lapata. Constructing semantic space models from parsed corpora. In ACL, volume 1 of ACL 03, pages 128--135. ACL, 2003.

Digital Library

[7]

C. A. Perfetti. The limits of co-occurrence: Tools and theories in language research. Discourse Processes, 25:363--377, 1998.

[8]

N. Raden. Start making sense: Get from data to semantic integration. Intelligent Enterprise, 10, October 2005.

[9]

R. Rapp. The computation of word associations: comparing syntagmatic and paradigmatic approaches. In Computational Linguistics, pages 1--7, Morristown, NJ, USA, 2002. Association for Computational Linguistics.

Digital Library

[10]

H. Schütze. Word space. In Advances in Neural Information Processing Systems 5, pages 895--902. Morgan Kaufmann, 1993.

Digital Library

[11]

N. Shadbolt, W. Hall, and T. Berners-Lee. The semantic web revisited. IEEE Intelligent Systems, pages 895--902, 2005.

Digital Library

[12]

M. Symonds, P. Bruza, L. Sitbon, and I. Turner. Modelling word meaning using efficient tensor representations. In PACLIC 25, pages 313--322, 2011.

[13]

P. D. Turney and P. Pantel. From frequency to meaning: vector space models of semantics. J. Artif. Int. Res., 37(1):141--188, Jan. 2010.

Digital Library

[14]

J. P. Van Overschelde, K. A. Rawson, and J. Dunlosky. Category norms: An updated and expanded version of the battig and montague (1969) norms. Journal of Memory and Language, 50(3), 2004.

[15]

J. Xu and W. B. Croft. Query expansion using local and global document analysis. In SIGIR'96, pages 4--11, New York, NY, USA, 1996. ACM.

Digital Library

Cited By

Symonds MBruza PSitbon L(2014)The efficiency of corpus-based distributional models for literature-based discovery on large data setsProceedings of the Second Australasian Web Conference - Volume 15510.5555/2667702.2667707(49-57)Online publication date: 20-Jan-2014
https://dl.acm.org/doi/10.5555/2667702.2667707
Symonds MBruza PZuccon GKoopman BSitbon LTurner I(2014)Automatic query expansion: A structural linguistic perspectiveJournal of the Association for Information Science and Technology10.1002/asi.2306565:8(1577-1596)Online publication date: 26-Feb-2014
https://doi.org/10.1002/asi.23065

Index Terms

A tensor encoding model for semantic processing
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Semantic networks

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Published In

cover image ACM Conferences

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

2840 pages

ISBN:9781450311564

DOI:10.1145/2396761

General Chair:
Xuewen Chen
Wayne State University, USA
,
Program Chairs:
Guy Lebanon
Georgia Institute of Technology
,
Haixun Wang
Microsoft Research Asia
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 29 October 2012

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CIKM'12: 21st ACM International Conference on Information and Knowledge Management

October 29 - November 2, 2012

Hawaii, Maui, USA

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Symonds MBruza PSitbon L(2014)The efficiency of corpus-based distributional models for literature-based discovery on large data setsProceedings of the Second Australasian Web Conference - Volume 15510.5555/2667702.2667707(49-57)Online publication date: 20-Jan-2014
https://dl.acm.org/doi/10.5555/2667702.2667707
Symonds MBruza PZuccon GKoopman BSitbon LTurner I(2014)Automatic query expansion: A structural linguistic perspectiveJournal of the Association for Information Science and Technology10.1002/asi.2306565:8(1577-1596)Online publication date: 26-Feb-2014
https://doi.org/10.1002/asi.23065

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