Detection of Affectively Comparable Term Using Hierarchical Knowledge and Blog Snippets

Ryosuke Yamanishi; Junichi Fukumoto; Fumito Masui

doi:10.20965/jaciii.2014.p0166

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JACIII Vol.18 No.2 pp. 166-174

doi: 10.20965/jaciii.2014.p0166

(2014)

Paper:

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Detection of Affectively Comparable Term Using Hierarchical Knowledge and Blog Snippets

Ryosuke Yamanishi^, Junichi Fukumoto^, and Fumito Masui^**

^*Department of Media Technology, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga 525-8577, Japan

^**Department of Computer Science, Kitami Institute of Technology, 165 Kouen-cho, Kitami, Hokkaido 090-8577, Japan

Received:

September 18, 2013

Accepted:

February 3, 2014

Published:

March 20, 2014

Keywords:

natural language processing, affective computing, knowledge discovery, web intelligence

Abstract

This paper describes a method for detecting affectively comparable terms. Comparable terms are often handled as sample objects instance in order to enrich linguistic expression, and using such terms explains and describes descriptions well. Coordinate terms in hierarchical knowledge are potentially comparable terms. Hierarchical coordinate terms are however sometimes affectively inappropriate as comparable term, because hierarchical knowledge is constructed by using only semantics without affections. We obtained the affections of terms obtained from blog and innovated them into hierarchical knowledge in order to detect affectively comparable terms. We conduct experiments to detect affectively comparable terms and discuss results, from which, we confirmed that affectively comparable terms could be detected by our proposed method. We deem detected affectively comparable terms to be applicable to creating artificial intelligence realizing intuitive interaction.

Cite this article as:

R. Yamanishi, J. Fukumoto, and F. Masui, “Detection of Affectively Comparable Term Using Hierarchical Knowledge and Blog Snippets,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.2, pp. 166-174, 2014.

Data files:

References

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[3] C. Fellbaum, “WordNet. An electronic lexical database,” MIT Press, 1998.
[4] F.Masui, J. Fukumoto, and K. Araki, “An Automatic Relevance Estimation of Property Values and Its Feedback Based onWorldWide Web for Metaphor Recognition,” J. of The Institute of Electronics, Information and Communication Engineers, J89-D(4), pp. 860-870, 2006 (in Japanese).
[5] F. Masui, Y. Kawamura, J. Fukumoto, and N. Isu, “MURASAKI: Web-based Word Sense Description System,” In Proc. of the 23rd Int. Technical Conf. on Circuits/Systems, Computers and Communications, pp. 1285.1288, 2008.
[6] H. Ooshima, S. Oyama, and K. Tanaka, “Searching Coordinate Terms with Their Context from the Web,” In Proc. of Web Information Systems (WISE 2006), Vol.LNCS 4255, pp. 40-47, 2006.
[7] P. Bouquet, L. Serani, and S. Zanobini, “Semantic coordination: A New Approach and an Application,” In Proc. of 2nd Int. Semantic Web Conference, pp. 130-145, 2003.
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[11] “YACIS: A Five-Billion-Word Corpus of Japanese Blogs Fully Annotated with Syntactic and Affective Information,” In Proc. of The AISB/IACAP World Congress 2012 in Honour of Alan Turing, 2nd Symp. on Linguistic and Cognitive Approaches To Dialog Agents (LaCATODA 2012), pp. 40-49, 2012.

[1] [1] F. Bond, T. Baldwin, R. Fothergill, and K. Uchimoto, “Japanese SemCor: A Sense-tagged Corpus of Japanese,” In Proc. of 6th Global WordNet Conf., pp. 56-63, 2012.

[2] [2] F. Bond, H. Isahara, S. Fujita, K. Uchimoto, T. Kuribayashi, and K. Kanzaki, “Enhancing the JapaneseWordNet,” In Proc. of the 7th Workshop on Asian Language Resources, pp. 1-8, 2009.

[3] [3] C. Fellbaum, “WordNet. An electronic lexical database,” MIT Press, 1998.

[4] [4] F.Masui, J. Fukumoto, and K. Araki, “An Automatic Relevance Estimation of Property Values and Its Feedback Based onWorldWide Web for Metaphor Recognition,” J. of The Institute of Electronics, Information and Communication Engineers, J89-D(4), pp. 860-870, 2006 (in Japanese).

[5] [5] F. Masui, Y. Kawamura, J. Fukumoto, and N. Isu, “MURASAKI: Web-based Word Sense Description System,” In Proc. of the 23rd Int. Technical Conf. on Circuits/Systems, Computers and Communications, pp. 1285.1288, 2008.

[6] [6] H. Ooshima, S. Oyama, and K. Tanaka, “Searching Coordinate Terms with Their Context from the Web,” In Proc. of Web Information Systems (WISE 2006), Vol.LNCS 4255, pp. 40-47, 2006.

[7] [7] P. Bouquet, L. Serani, and S. Zanobini, “Semantic coordination: A New Approach and an Application,” In Proc. of 2nd Int. Semantic Web Conference, pp. 130-145, 2003.

[8] [8] D. W. Harman, “An Experimental Study of Factors Important in Document Ranking,” In Proc. of the 9th Annual Int. ACM SIGIR Conf. on Research and development in Information Retrieval, pp. 186-193, 1986.

[9] [9] G. Salton, A. Wong, and C. S. Yang, “A vector space model for information retrieval,” Communications of the ACM, Vol.18, No.11, pp. 613-620, 1975.

[10] [10] A. Sumida, N. Yoshinaga, and K. Torisawa, “Hyponymy Relation Acquisition from Hierarchical Layouts in Wikipedia,” J. of Natural Language Processing, Vol.16, No.3, pp. 3-24, 2009 (in Japanese).

[11] [11] “YACIS: A Five-Billion-Word Corpus of Japanese Blogs Fully Annotated with Syntactic and Affective Information,” In Proc. of The AISB/IACAP World Congress 2012 in Honour of Alan Turing, 2nd Symp. on Linguistic and Cognitive Approaches To Dialog Agents (LaCATODA 2012), pp. 40-49, 2012.

Detection of Affectively Comparable Term Using Hierarchical Knowledge and Blog Snippets

Ryosuke Yamanishi*, Junichi Fukumoto*, and Fumito Masui**

Ryosuke Yamanishi^, Junichi Fukumoto^, and Fumito Masui^**