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        <title>Semantic overfitting: what ‘world’ do we consider when evaluating disambiguation of text?</title>
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    <name type="personal">
        <namePart type="given">Filip</namePart>
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    <name type="personal">
        <namePart type="given">Marten</namePart>
        <namePart type="family">Postma</namePart>
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    <name type="personal">
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <abstract>Semantic text processing faces the challenge of defining the relation between lexical expressions and the world to which they make reference within a period of time. It is unclear whether the current test sets used to evaluate disambiguation tasks are representative for the full complexity considering this time-anchored relation, resulting in semantic overfitting to a specific period and the frequent phenomena within. We conceptualize and formalize a set of metrics which evaluate this complexity of datasets. We provide evidence for their applicability on five different disambiguation tasks. To challenge semantic overfitting of disambiguation systems, we propose a time-based, metric-aware method for developing datasets in a systematic and semi-automated manner, as well as an event-based QA task.</abstract>
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%0 Conference Proceedings
%T Semantic overfitting: what ‘world’ do we consider when evaluating disambiguation of text?
%A Ilievski, Filip
%A Postma, Marten
%A Vossen, Piek
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F ilievski-etal-2016-semantic
%X Semantic text processing faces the challenge of defining the relation between lexical expressions and the world to which they make reference within a period of time. It is unclear whether the current test sets used to evaluate disambiguation tasks are representative for the full complexity considering this time-anchored relation, resulting in semantic overfitting to a specific period and the frequent phenomena within. We conceptualize and formalize a set of metrics which evaluate this complexity of datasets. We provide evidence for their applicability on five different disambiguation tasks. To challenge semantic overfitting of disambiguation systems, we propose a time-based, metric-aware method for developing datasets in a systematic and semi-automated manner, as well as an event-based QA task.
%U https://aclanthology.org/C16-1112
%P 1180-1191

Markdown (Informal)

[Semantic overfitting: what ‘world’ do we consider when evaluating disambiguation of text?](https://aclanthology.org/C16-1112) (Ilievski et al., COLING 2016)

• Semantic overfitting: what ‘world’ do we consider when evaluating disambiguation of text? (Ilievski et al., COLING 2016)

ACL

• Filip Ilievski, Marten Postma, and Piek Vossen. 2016. Semantic overfitting: what ‘world’ do we consider when evaluating disambiguation of text?. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1180–1191, Osaka, Japan. The COLING 2016 Organizing Committee.