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Towards semantic category verification with arbitrary precision

Roussinov, Dmitri (2011) Towards semantic category verification with arbitrary precision. In: Advances in Information Retrieval Theory. Lecture Notes in Computer Science, Springer, pp. 274-284. ISBN 9783642233173

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Abstract

Many tasks related to or supporting information retrieval, such as query expansion, automated question answering, reasoning, or heterogeneous database integration, involve verification of a semantic category (e.g. “coffee” is a drink, “red” is a color, while “steak” is not a drink and “big” is not a color). We present a novel framework to automatically validate a membership in an arbitrary, not a trained a priori semantic category up to a desired level of accuracy. Our approach does not rely on any manually codified knowledge but instead capitalizes on the diversity of topics and word usage in a large corpus (e.g. World Wide Web). Using TREC factoid questions that expect the answer to belong to a specific semantic category, we show that a very high level of accuracy can be reached by automatically identifying more training seeds and more training patterns when needed. We develop a specific quantitative validation model that takes uncertainty and redundancy in the training data into consideration. We empirically confirm the important aspects of our model through ablation studies.

Item type: Book Section
ID code: 45793
Keywords: information retrieval, IR, semantic searching, semantic category verification, Electronic computers. Computer science, Library Science. Information Science, Human-Computer Interaction
Subjects: Science > Mathematics > Electronic computers. Computer science
Bibliography. Library Science. Information Resources > Library Science. Information Science
Department: Faculty of Science > Computer and Information Sciences
Depositing user: Pure Administrator
Date deposited: 11 Nov 2013 10:28
Last modified: 22 Mar 2017 15:45
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URI: https://strathprints.strath.ac.uk/id/eprint/45793
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