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        <title>ClearTAC: Verb Tense, Aspect, and Form Classification Using Neural Nets</title>
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    <name type="personal">
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            <title>Proceedings of the First International Workshop on Designing Meaning Representations</title>
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            <namePart type="given">Nianwen</namePart>
            <namePart type="family">Xue</namePart>
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            <namePart type="given">William</namePart>
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            <namePart type="given">Jan</namePart>
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            <namePart type="given">Chu-Ren</namePart>
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    <abstract>This paper proposes using a Bidirectional LSTM-CRF model in order to identify the tense and aspect of verbs. The information that this classifier outputs can be useful for ordering events and can provide a pre-processing step to improve efficiency of annotating this type of information. This neural network architecture has been successfully employed for other sequential labeling tasks, and we show that it significantly outperforms the rule-based tool TMV-annotator on the Propbank I dataset.</abstract>
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%0 Conference Proceedings
%T ClearTAC: Verb Tense, Aspect, and Form Classification Using Neural Nets
%A Myers, Skatje
%A Palmer, Martha
%Y Xue, Nianwen
%Y Croft, William
%Y Hajic, Jan
%Y Huang, Chu-Ren
%Y Oepen, Stephan
%Y Palmer, Martha
%Y Pustejovksy, James
%S Proceedings of the First International Workshop on Designing Meaning Representations
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F myers-palmer-2019-cleartac
%X This paper proposes using a Bidirectional LSTM-CRF model in order to identify the tense and aspect of verbs. The information that this classifier outputs can be useful for ordering events and can provide a pre-processing step to improve efficiency of annotating this type of information. This neural network architecture has been successfully employed for other sequential labeling tasks, and we show that it significantly outperforms the rule-based tool TMV-annotator on the Propbank I dataset.
%R 10.18653/v1/W19-3315
%U https://aclanthology.org/W19-3315
%U https://doi.org/10.18653/v1/W19-3315
%P 136-140

Markdown (Informal)

[ClearTAC: Verb Tense, Aspect, and Form Classification Using Neural Nets](https://aclanthology.org/W19-3315) (Myers & Palmer, DMR 2019)

• ClearTAC: Verb Tense, Aspect, and Form Classification Using Neural Nets (Myers & Palmer, DMR 2019)

ACL

• Skatje Myers and Martha Palmer. 2019. ClearTAC: Verb Tense, Aspect, and Form Classification Using Neural Nets. In Proceedings of the First International Workshop on Designing Meaning Representations, pages 136–140, Florence, Italy. Association for Computational Linguistics.