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        <title>On the contribution of word embeddings to temporal relation classification</title>
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    <name type="personal">
        <namePart type="given">Paramita</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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        <name type="personal">
            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <abstract>Temporal relation classification is a challenging task, especially when there are no explicit markers to characterise the relation between temporal entities. This occurs frequently in inter-sentential relations, whose entities are not connected via direct syntactic relations making classification even more difficult. In these cases, resorting to features that focus on the semantic content of the event words may be very beneficial for inferring implicit relations. Specifically, while morpho-syntactic and context features are considered sufficient for classifying event-timex pairs, we believe that exploiting distributional semantic information about event words can benefit supervised classification of other types of pairs. In this work, we assess the impact of using word embeddings as features for event words in classifying temporal relations of event-event pairs and event-DCT (document creation time) pairs.</abstract>
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%0 Conference Proceedings
%T On the contribution of word embeddings to temporal relation classification
%A Mirza, Paramita
%A Tonelli, Sara
%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 mirza-tonelli-2016-contribution
%X Temporal relation classification is a challenging task, especially when there are no explicit markers to characterise the relation between temporal entities. This occurs frequently in inter-sentential relations, whose entities are not connected via direct syntactic relations making classification even more difficult. In these cases, resorting to features that focus on the semantic content of the event words may be very beneficial for inferring implicit relations. Specifically, while morpho-syntactic and context features are considered sufficient for classifying event-timex pairs, we believe that exploiting distributional semantic information about event words can benefit supervised classification of other types of pairs. In this work, we assess the impact of using word embeddings as features for event words in classifying temporal relations of event-event pairs and event-DCT (document creation time) pairs.
%U https://aclanthology.org/C16-1265
%P 2818-2828

Markdown (Informal)

[On the contribution of word embeddings to temporal relation classification](https://aclanthology.org/C16-1265) (Mirza & Tonelli, COLING 2016)

• On the contribution of word embeddings to temporal relation classification (Mirza & Tonelli, COLING 2016)

ACL

• Paramita Mirza and Sara Tonelli. 2016. On the contribution of word embeddings to temporal relation classification. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2818–2828, Osaka, Japan. The COLING 2016 Organizing Committee.