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        <title>Lin: Unsupervised Extraction of Tasks from Textual Communication</title>
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        <namePart type="given">Parth</namePart>
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    <name type="personal">
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        <namePart type="family">Singh</namePart>
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    <name type="personal">
        <namePart type="given">Anup</namePart>
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            <namePart type="given">Chengqing</namePart>
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    <abstract>Commitments and requests are a hallmark of collaborative communication, especially in team settings. Identifying specific tasks being committed to or request from emails and chat messages can enable important downstream tasks, such as producing todo lists, reminders, and calendar entries. State-of-the-art approaches for task identification rely on large annotated datasets, which are not always available, especially for domain-specific tasks. Accordingly, we propose Lin, an unsupervised approach of identifying tasks that leverages dependency parsing and VerbNet. Our evaluations show that Lin yields comparable or more accurate results than supervised models on domains with large training sets, and maintains its excellent performance on unseen domains.</abstract>
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        <date>2020-12</date>
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            <start>1815</start>
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%0 Conference Proceedings
%T Lin: Unsupervised Extraction of Tasks from Textual Communication
%A Diwanji, Parth
%A Guo, Hui
%A Singh, Munindar
%A Kalia, Anup
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F diwanji-etal-2020-lin
%X Commitments and requests are a hallmark of collaborative communication, especially in team settings. Identifying specific tasks being committed to or request from emails and chat messages can enable important downstream tasks, such as producing todo lists, reminders, and calendar entries. State-of-the-art approaches for task identification rely on large annotated datasets, which are not always available, especially for domain-specific tasks. Accordingly, we propose Lin, an unsupervised approach of identifying tasks that leverages dependency parsing and VerbNet. Our evaluations show that Lin yields comparable or more accurate results than supervised models on domains with large training sets, and maintains its excellent performance on unseen domains.
%R 10.18653/v1/2020.coling-main.164
%U https://aclanthology.org/2020.coling-main.164
%U https://doi.org/10.18653/v1/2020.coling-main.164
%P 1815-1819

Markdown (Informal)

[Lin: Unsupervised Extraction of Tasks from Textual Communication](https://aclanthology.org/2020.coling-main.164) (Diwanji et al., COLING 2020)

• Lin: Unsupervised Extraction of Tasks from Textual Communication (Diwanji et al., COLING 2020)

ACL

• Parth Diwanji, Hui Guo, Munindar Singh, and Anup Kalia. 2020. Lin: Unsupervised Extraction of Tasks from Textual Communication. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1815–1819, Barcelona, Spain (Online). International Committee on Computational Linguistics.