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        <title>Learning to Define Terms in the Software Domain</title>
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    <name type="personal">
        <namePart type="given">Vidhisha</namePart>
        <namePart type="family">Balachandran</namePart>
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    <name type="personal">
        <namePart type="given">Dheeraj</namePart>
        <namePart type="family">Rajagopal</namePart>
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    <name type="personal">
        <namePart type="given">Rose</namePart>
        <namePart type="given">Catherine</namePart>
        <namePart type="family">Kanjirathinkal</namePart>
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    <name type="personal">
        <namePart type="given">William</namePart>
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        <dateIssued>2018-11</dateIssued>
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            <title>Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text</title>
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            <namePart type="given">Wei</namePart>
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            <namePart type="given">Alan</namePart>
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    <abstract>One way to test a person’s knowledge of a domain is to ask them to define domain-specific terms. Here, we investigate the task of automatically generating definitions of technical terms by reading text from the technical domain. Specifically, we learn definitions of software entities from a large corpus built from the user forum Stack Overflow. To model definitions, we train a language model and incorporate additional domain-specific information like word co-occurrence, and ontological category information. Our approach improves previous baselines by 2 BLEU points for the definition generation task. Our experiments also show the additional challenges associated with the task and the short-comings of language-model based architectures for definition generation.</abstract>
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    <identifier type="doi">10.18653/v1/W18-6122</identifier>
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%0 Conference Proceedings
%T Learning to Define Terms in the Software Domain
%A Balachandran, Vidhisha
%A Rajagopal, Dheeraj
%A Kanjirathinkal, Rose Catherine
%A Cohen, William
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F balachandran-etal-2018-learning
%X One way to test a person’s knowledge of a domain is to ask them to define domain-specific terms. Here, we investigate the task of automatically generating definitions of technical terms by reading text from the technical domain. Specifically, we learn definitions of software entities from a large corpus built from the user forum Stack Overflow. To model definitions, we train a language model and incorporate additional domain-specific information like word co-occurrence, and ontological category information. Our approach improves previous baselines by 2 BLEU points for the definition generation task. Our experiments also show the additional challenges associated with the task and the short-comings of language-model based architectures for definition generation.
%R 10.18653/v1/W18-6122
%U https://aclanthology.org/W18-6122/
%U https://doi.org/10.18653/v1/W18-6122
%P 164-172

Markdown (Informal)

[Learning to Define Terms in the Software Domain](https://aclanthology.org/W18-6122/) (Balachandran et al., WNUT 2018)

• Learning to Define Terms in the Software Domain (Balachandran et al., WNUT 2018)

ACL

• Vidhisha Balachandran, Dheeraj Rajagopal, Rose Catherine Kanjirathinkal, and William Cohen. 2018. Learning to Define Terms in the Software Domain. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, pages 164–172, Brussels, Belgium. Association for Computational Linguistics.