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    <titleInfo>
        <title>Definition Extraction Feature Analysis: From Canonical to Naturally-Occurring Definitions</title>
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    <name type="personal">
        <namePart type="given">Mireia</namePart>
        <namePart type="family">Roig Mirapeix</namePart>
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    <name type="personal">
        <namePart type="given">Luis</namePart>
        <namePart type="family">Espinosa Anke</namePart>
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    <name type="personal">
        <namePart type="given">Jose</namePart>
        <namePart type="family">Camacho-Collados</namePart>
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        <dateIssued>2020-12</dateIssued>
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            <title>Proceedings of the Workshop on the Cognitive Aspects of the Lexicon</title>
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        <name type="personal">
            <namePart type="given">Michael</namePart>
            <namePart type="family">Zock</namePart>
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        <name type="personal">
            <namePart type="given">Emmanuele</namePart>
            <namePart type="family">Chersoni</namePart>
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        <name type="personal">
            <namePart type="given">Alessandro</namePart>
            <namePart type="family">Lenci</namePart>
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        <name type="personal">
            <namePart type="given">Enrico</namePart>
            <namePart type="family">Santus</namePart>
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    <abstract>Textual definitions constitute a fundamental source of knowledge when seeking the meaning of words, and they are the cornerstone of lexical resources like glossaries, dictionaries, encyclopedia or thesauri. In this paper, we present an in-depth analytical study on the main features relevant to the task of definition extraction. Our main goal is to study whether linguistic structures from canonical (the Aristotelian or genus et differentia model) can be leveraged to retrieve definitions from corpora in different domains of knowledge and textual genres alike. To this end, we develop a simple linear classifier and analyze the contribution of several (sets of) linguistic features. Finally, as a result of our experiments, we also shed light on the particularities of existing benchmarks as well as the most challenging aspects of the task.</abstract>
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        <date>2020-12</date>
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            <start>81</start>
            <end>91</end>
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%0 Conference Proceedings
%T Definition Extraction Feature Analysis: From Canonical to Naturally-Occurring Definitions
%A Roig Mirapeix, Mireia
%A Espinosa Anke, Luis
%A Camacho-Collados, Jose
%Y Zock, Michael
%Y Chersoni, Emmanuele
%Y Lenci, Alessandro
%Y Santus, Enrico
%S Proceedings of the Workshop on the Cognitive Aspects of the Lexicon
%D 2020
%8 December
%I Association for Computational Linguistics
%C Online
%F roig-mirapeix-etal-2020-definition
%X Textual definitions constitute a fundamental source of knowledge when seeking the meaning of words, and they are the cornerstone of lexical resources like glossaries, dictionaries, encyclopedia or thesauri. In this paper, we present an in-depth analytical study on the main features relevant to the task of definition extraction. Our main goal is to study whether linguistic structures from canonical (the Aristotelian or genus et differentia model) can be leveraged to retrieve definitions from corpora in different domains of knowledge and textual genres alike. To this end, we develop a simple linear classifier and analyze the contribution of several (sets of) linguistic features. Finally, as a result of our experiments, we also shed light on the particularities of existing benchmarks as well as the most challenging aspects of the task.
%U https://aclanthology.org/2020.cogalex-1.10
%P 81-91

Markdown (Informal)

[Definition Extraction Feature Analysis: From Canonical to Naturally-Occurring Definitions](https://aclanthology.org/2020.cogalex-1.10) (Roig Mirapeix et al., CogALex 2020)

• Definition Extraction Feature Analysis: From Canonical to Naturally-Occurring Definitions (Roig Mirapeix et al., CogALex 2020)

ACL

• Mireia Roig Mirapeix, Luis Espinosa Anke, and Jose Camacho-Collados. 2020. Definition Extraction Feature Analysis: From Canonical to Naturally-Occurring Definitions. In Proceedings of the Workshop on the Cognitive Aspects of the Lexicon, pages 81–91, Online. Association for Computational Linguistics.