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Combining heterogeneous knowledge resources for improved distributional semantic models

Szarvas, György and Zesch, Torsten and Gurevych, Iryna
Gelbukh, Alexander (ed.) (2011):
Combining heterogeneous knowledge resources for improved distributional semantic models.
In: Proceedings of the 12th International Conference on Intelligent Text Processing and Computational Linguistics, Springer, pp. 289-303, [Online-Edition: https://www.springer.com/gp/book/9783642193996],
[Book Section]

Abstract

The Explicit Semantic Analysis (ESA) model based on term cooccurrences in Wikipedia has been regarded as state-of-the-art semantic relatedness measure in the recent years. We provide an analysis of the important parameters of ESA using datasets in five different languages. Additionally, we propose the use of ESA with multiple lexical semantic resources thus exploiting multiple evidence of term cooccurrence to improve over the Wikipedia-based measure. Exploiting the improved robustness and coverage of the proposed combination, we report improved performance over single resources in word semantic relatedness, solving word choice problems, classification of semantic relations between nominals, and text similarity.

Item Type: Book Section
Erschienen: 2011
Editors: Gelbukh, Alexander
Creators: Szarvas, György and Zesch, Torsten and Gurevych, Iryna
Title: Combining heterogeneous knowledge resources for improved distributional semantic models
Language: English
Abstract:

The Explicit Semantic Analysis (ESA) model based on term cooccurrences in Wikipedia has been regarded as state-of-the-art semantic relatedness measure in the recent years. We provide an analysis of the important parameters of ESA using datasets in five different languages. Additionally, we propose the use of ESA with multiple lexical semantic resources thus exploiting multiple evidence of term cooccurrence to improve over the Wikipedia-based measure. Exploiting the improved robustness and coverage of the proposed combination, we report improved performance over single resources in word semantic relatedness, solving word choice problems, classification of semantic relations between nominals, and text similarity.

Title of Book: Proceedings of the 12th International Conference on Intelligent Text Processing and Computational Linguistics
Series Name: Lecture Notes in Computer Science
Volume: 6608
Publisher: Springer
Uncontrolled Keywords: UKP_p_ASC;UKP_p_SIGMUND
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
Date Deposited: 31 Dec 2016 14:29
Official URL: https://www.springer.com/gp/book/9783642193996
Identification Number: TUD-CS-2011-0069
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