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        <title>‘Calling on the classical phone’: a distributional model of adjective-noun errors in learners’ English</title>
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        <namePart type="given">Aurélie</namePart>
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            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
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    <abstract>In this paper we discuss three key points related to error detection (ED) in learners’ English. We focus on content word ED as one of the most challenging tasks in this area, illustrating our claims on adjective–noun (AN) combinations. In particular, we (1) investigate the role of context in accurately capturing semantic anomalies and implement a system based on distributional topic coherence, which achieves state-of-the-art accuracy on a standard test set; (2) thoroughly investigate our system’s performance across individual adjective classes, concluding that a class-dependent approach is beneficial to the task; (3) discuss the data size bottleneck in this area, and highlight the challenges of automatic error generation for content words.</abstract>
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%0 Conference Proceedings
%T ‘Calling on the classical phone’: a distributional model of adjective-noun errors in learners’ English
%A Herbelot, Aurélie
%A Kochmar, Ekaterina
%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 herbelot-kochmar-2016-calling
%X In this paper we discuss three key points related to error detection (ED) in learners’ English. We focus on content word ED as one of the most challenging tasks in this area, illustrating our claims on adjective–noun (AN) combinations. In particular, we (1) investigate the role of context in accurately capturing semantic anomalies and implement a system based on distributional topic coherence, which achieves state-of-the-art accuracy on a standard test set; (2) thoroughly investigate our system’s performance across individual adjective classes, concluding that a class-dependent approach is beneficial to the task; (3) discuss the data size bottleneck in this area, and highlight the challenges of automatic error generation for content words.
%U https://aclanthology.org/C16-1093
%P 976-986

Markdown (Informal)

[‘Calling on the classical phone’: a distributional model of adjective-noun errors in learners’ English](https://aclanthology.org/C16-1093) (Herbelot & Kochmar, COLING 2016)

• ‘Calling on the classical phone’: a distributional model of adjective-noun errors in learners’ English (Herbelot & Kochmar, COLING 2016)

ACL

• Aurélie Herbelot and Ekaterina Kochmar. 2016. ‘Calling on the classical phone’: a distributional model of adjective-noun errors in learners’ English. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 976–986, Osaka, Japan. The COLING 2016 Organizing Committee.