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        <title>Linguistic features for Hindi light verb construction identification</title>
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    <name type="personal">
        <namePart type="given">Ashwini</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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            <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>Light verb constructions (LVC) in Hindi are highly productive. If we can distinguish a case such as nirnay lenaa ‘decision take; decide’ from an ordinary verb-argument combination kaagaz lenaa ‘paper take; take (a) paper’,it has been shown to aid NLP applications such as parsing (Begum et al., 2011) and machine translation (Pal et al., 2011). In this paper, we propose an LVC identification system using language specific features for Hindi which shows an improvement over previous work(Begum et al., 2011). To build our system, we carry out a linguistic analysis of Hindi LVCs using Hindi Treebank annotations and propose two new features that are aimed at capturing the diversity of Hindi LVCs in the corpus. We find that our model performs robustly across a diverse range of LVCs and our results underscore the importance of semantic features, which is in keeping with the findings for English. Our error analysis also demonstrates that our classifier can be used to further refine LVC annotations in the Hindi Treebank and make them more consistent across the board.</abstract>
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%0 Conference Proceedings
%T Linguistic features for Hindi light verb construction identification
%A Vaidya, Ashwini
%A Agarwal, Sumeet
%A Palmer, Martha
%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 vaidya-etal-2016-linguistic
%X Light verb constructions (LVC) in Hindi are highly productive. If we can distinguish a case such as nirnay lenaa ‘decision take; decide’ from an ordinary verb-argument combination kaagaz lenaa ‘paper take; take (a) paper’,it has been shown to aid NLP applications such as parsing (Begum et al., 2011) and machine translation (Pal et al., 2011). In this paper, we propose an LVC identification system using language specific features for Hindi which shows an improvement over previous work(Begum et al., 2011). To build our system, we carry out a linguistic analysis of Hindi LVCs using Hindi Treebank annotations and propose two new features that are aimed at capturing the diversity of Hindi LVCs in the corpus. We find that our model performs robustly across a diverse range of LVCs and our results underscore the importance of semantic features, which is in keeping with the findings for English. Our error analysis also demonstrates that our classifier can be used to further refine LVC annotations in the Hindi Treebank and make them more consistent across the board.
%U https://aclanthology.org/C16-1125/
%P 1320-1329

Markdown (Informal)

[Linguistic features for Hindi light verb construction identification](https://aclanthology.org/C16-1125/) (Vaidya et al., COLING 2016)

• Linguistic features for Hindi light verb construction identification (Vaidya et al., COLING 2016)

ACL

• Ashwini Vaidya, Sumeet Agarwal, and Martha Palmer. 2016. Linguistic features for Hindi light verb construction identification. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1320–1329, Osaka, Japan. The COLING 2016 Organizing Committee.