Domain Adaptive Relation Extraction Based on Seeds

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Note: This list is heavily outdated. For current publications of our lab, see here.

  • Semantic Rule Filtering for Web-Scale Relation Extraction (2013)

    Authors: Andrea Moro, Hong Li, Sebastian Krause, Feiyu Xu, Roberto Navigli, Hans Uszkoreit
     
    Proceedings of the 12th International Semantic Web Conference, Sydney, Australia, Springer, 10/2013
     
    Abstract: Web-scale relation extraction is a means for building and extending large repositories of formalized knowledge.
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  • Large-Scale Learning of Relation-Extraction Rules with Distant Supervision from the Web (2012)

    Authors: Sebastian Krause, Hong Li, Feiyu Xu, Hans Uszkoreit
     
    Proceedings of the 11th International Semantic Web Conference, Boston, United States, Springer, 11/2012
     
    Abstract: We present a large-scale relation extraction (RE) system which learns grammar-based RE rules from the Web by utilizing large numbers of relation instances as seed.
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  • Minimally Supervised Domain-Adaptive Parse Reranking for Relation Extraction (2011)

    Authors: Feiyu Xu, Hong Li, Yi Zhang, Hans Uszkoreit, Sebastian Krause
     
    Proceedings of International Workshop on Parsing Technologies,2011, Dublin
     
    Abstract: The paper demonstrates how the generic parser of a minimally supervised information extraction framework can be adapted to a given task and domain for relation extraction (RE).
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  • Minimally Supervised Rule Learning for the Extraction of Biographic Information from Various Social Domains (2011)

    Authors: Hong Li, Feiyu Xu, Hans Uszkoreit
     
    Proceedings of the International Conference on Recent Advances in Natural Language Processing 2011, Hissar, Bulgaria
     
    Abstract: This paper investigates the application of an existing seed-based minimally supervised learning algorithm to different social domains exhibiting different properties of the available data. A systematic analysis studies the respective data properties of the three domains including the distribution of the semantic arguments and their combinations.
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  • META-DARE: Monitoring the Minimally Supervised ML of Relation Extraction Rules (2011)

    Authors: Hong Li, Feiyu Xu, Hans Uszkoreit
     
    Proceedings of the International Conference on Recent Advances in Natural Language Processing 2011, Hissar, Bulgaria
     
    Abstract: This paper demonstrates a web-based online system, called META-DARE. META-DARE is built to assist researchers to obtain insights into seed-based minimally supervised machine learning for relation extraction.
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  • Learning Relation Extraction Grammars with Minimal Human Intervention: Strategy, Results, Insights and Plans (2011)

    Authors: Hans Uszkoreit
     
    Computational Linguistics and Intelligent Text Processing
     
    Abstract: The paper describes the operation and evolution of a linguistically oriented framework for the minimally supervised learning of relation extraction grammars from textual data.
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  • Boosting Relation Extraction with Limited Closed-World Knowledge (2010)

    Authors: Feiyu Xu, Hans Uszkoreit, Sebastian Krause, Hong Li
     
    Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010), Beijing, China
     
    Abstract: The main contribution of this paper is a systematic analysis of a minimally supervised machine learning method for relation extraction grammars.
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  • Analysis and Improvement of Minimally Supervised Machine Learning for Relation Extraction (2009)

    Authors: Hans Uszkoreit, Feiyu Xu, Hong Li
     
    Proceedings of the 14th International Conference on Applications of Natural Language to Information Systems, NLDB 2009
     
    Abstract: The main contribution of this paper is a systematic analysis of a minimally supervised machine learning method for relation extraction grammars.
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  • Task driven coreference resolution for relation extraction (2008)

    Authors: Feiyu Xu, Hans Uszkoreit, Hong Li
     
    Proceedings of the European Conference for Artificial Inteligence ECAI 2008, Patras, Greece, 8/2008
     
    Abstract: This paper presents the extension of an existing mimimally supervised rule acquisition method for relation extraction by coreference resolution (CR).
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  • Adaptation of Relation Extraction Rules to New Domains (2008)

    Authors: Feiyu Xu, Hans Uszkoreit, Hong Li, Niko Felger
     
    Proceedings of the Poster Session of the Sixth International Conference on Language Resources and Evaluation, LREC 2008, Marrekech, Morocco, 5/2008
     
    Abstract: This paper presents various strategies to improve the extraction performance of less prominent relations with help of the rules learned for some similar relations, for which a large amount of data with suitable data properties is available.
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  • Bootstrapping Relation Extraction from Semantic Seeds (2007)

    Authors: Feiyu Xu
     
    PhD Thesis in the Computational Linguistics at Saarland University, 12/2007
     
  • A Seed-driven Bottom-up Machine Learning Framework for Extracting Relations of Various Complexity (2007)

    Authors: Feiyu Xu, Hans Uszkoreit, Hong Li
     
    Proceedings of ACL 2007, 45th Annual Meeting of the Association for Computational Linguistics, Prague, Czech Republic, 6/2007
     
    Abstract: A minimally supervised machine learning framework is described for extracting relations of various complexity.
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  • Automatic Event and Relation Detection with Seeds of Varying Complexity (2006)

    Authors: Feiyu Xu, Hans Uszkoreit, Hong Li
     
    AAAI Workshop Event Extraction and Synthesis, Boston, 7/2006
     
    Abstract: In this paper, we present an approach for automatically detecting events in natural language texts by learning patterns that signal the mentioning of such events.
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