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        <title>Utilization of Multiple Language Resources for Robust Grammar-Based Tense and Aspect Classification</title>
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        <namePart type="given">Alexis</namePart>
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    <name type="personal">
        <namePart type="given">Carlota</namePart>
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            <title>Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)</title>
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        <name type="personal">
            <namePart type="given">Maria</namePart>
            <namePart type="given">Teresa</namePart>
            <namePart type="family">Lino</namePart>
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            <namePart type="given">Maria</namePart>
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            <namePart type="given">Rute</namePart>
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            <namePart type="given">Raquel</namePart>
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    <abstract>This paper reports on an ongoing project that uses varied language resources and advanced NLP tools for a linguistic classification task in discourse semantics. The system we present is designed to assign a “situation entity” class label to each predicator in English text. The project goal is to achieve the best-possible identification of situation entities in naturally-occurring written texts by implementing a robust system that will deal with real corpus material, rather than just with constructed textbook examples of discourse. In this paper we focus on the combination of multiple information sources, which we see as being vital for a robust classification system. We use a deep syntactic grammar of English to identify morphological, syntactic, and discourse clues, and we use various lexical databases for fine-grained semantic properties of the predicators. Experiments performed to date show that enhancing the output of the grammar with information from lexical resources improves recall but lowers precision in the situation entity classification task.</abstract>
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%0 Conference Proceedings
%T Utilization of Multiple Language Resources for Robust Grammar-Based Tense and Aspect Classification
%A Palmer, Alexis
%A Kuhn, Jonas
%A Smith, Carlota
%Y Lino, Maria Teresa
%Y Xavier, Maria Francisca
%Y Ferreira, Fátima
%Y Costa, Rute
%Y Silva, Raquel
%S Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)
%D 2004
%8 May
%I European Language Resources Association (ELRA)
%C Lisbon, Portugal
%F palmer-etal-2004-utilization
%X This paper reports on an ongoing project that uses varied language resources and advanced NLP tools for a linguistic classification task in discourse semantics. The system we present is designed to assign a “situation entity” class label to each predicator in English text. The project goal is to achieve the best-possible identification of situation entities in naturally-occurring written texts by implementing a robust system that will deal with real corpus material, rather than just with constructed textbook examples of discourse. In this paper we focus on the combination of multiple information sources, which we see as being vital for a robust classification system. We use a deep syntactic grammar of English to identify morphological, syntactic, and discourse clues, and we use various lexical databases for fine-grained semantic properties of the predicators. Experiments performed to date show that enhancing the output of the grammar with information from lexical resources improves recall but lowers precision in the situation entity classification task.
%U http://www.lrec-conf.org/proceedings/lrec2004/pdf/402.pdf

Markdown (Informal)

[Utilization of Multiple Language Resources for Robust Grammar-Based Tense and Aspect Classification](http://www.lrec-conf.org/proceedings/lrec2004/pdf/402.pdf) (Palmer et al., LREC 2004)

• Utilization of Multiple Language Resources for Robust Grammar-Based Tense and Aspect Classification (Palmer et al., LREC 2004)

ACL

• Alexis Palmer, Jonas Kuhn, and Carlota Smith. 2004. Utilization of Multiple Language Resources for Robust Grammar-Based Tense and Aspect Classification. In Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04), Lisbon, Portugal. European Language Resources Association (ELRA).