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Semantic verification in an online fact seeking environment

Published: 06 November 2007 Publication History

Abstract

Many artificial intelligence tasks, such as 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 algorithm to automatically validate a semantic category. Contrary to the methods suggested earlier, our approach does not rely on any manually codified knowledge but instead capitalizes on the diversity of topics and word usage on the World Wide Web. We have tested our approach within our online fact-seeking (question answering) environment. When tested on the TREC questions that expect the answer to belong to a specific semantic category, our approach has improved the accuracy by up to 14% depending on the model and metrics used.

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Cited By

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  • (2023)Fine-tuning language models to recognize semantic relationsLanguage Resources and Evaluation10.1007/s10579-023-09677-w57:4(1463-1486)Online publication date: 23-Jul-2023
  • (2019)Combining Neural Networks and Pattern Matching for Ontology Mining - a Meta Learning Inspired Approach2019 IEEE 13th International Conference on Semantic Computing (ICSC)10.1109/ICOSC.2019.8665528(63-70)Online publication date: Jan-2019
  • (2011)Towards semantic category verification with arbitrary precisionProceedings of the Third international conference on Advances in information retrieval theory10.5555/2040317.2040351(274-284)Online publication date: 12-Sep-2011
  • Show More Cited By

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    cover image ACM Conferences
    CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
    November 2007
    1048 pages
    ISBN:9781595938039
    DOI:10.1145/1321440
    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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    Publication History

    Published: 06 November 2007

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    Author Tags

    1. artificial intelligence
    2. online search engines
    3. question answering

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    View all
    • (2023)Fine-tuning language models to recognize semantic relationsLanguage Resources and Evaluation10.1007/s10579-023-09677-w57:4(1463-1486)Online publication date: 23-Jul-2023
    • (2019)Combining Neural Networks and Pattern Matching for Ontology Mining - a Meta Learning Inspired Approach2019 IEEE 13th International Conference on Semantic Computing (ICSC)10.1109/ICOSC.2019.8665528(63-70)Online publication date: Jan-2019
    • (2011)Towards semantic category verification with arbitrary precisionProceedings of the Third international conference on Advances in information retrieval theory10.5555/2040317.2040351(274-284)Online publication date: 12-Sep-2011
    • (2011)Towards Semantic Category Verification with Arbitrary PrecisionAdvances in Information Retrieval Theory10.1007/978-3-642-23318-0_25(274-284)Online publication date: 2011

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