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        <title>Deep Learning Architecture for Complex Word Identification</title>
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    <name type="personal">
        <namePart type="given">Dirk</namePart>
        <namePart type="family">De Hertog</namePart>
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    <name type="personal">
        <namePart type="given">Anaïs</namePart>
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            <title>Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications</title>
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        <name type="personal">
            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
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        <name type="personal">
            <namePart type="given">Jill</namePart>
            <namePart type="family">Burstein</namePart>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
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        <name type="personal">
            <namePart type="given">Claudia</namePart>
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        <name type="personal">
            <namePart type="given">Helen</namePart>
            <namePart type="family">Yannakoudakis</namePart>
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    <abstract>We describe a system for the CWI-task that includes information on 5 aspects of the (complex) lexical item, namely distributional information of the item itself, morphological structure, psychological measures, corpus-counts and topical information. We constructed a deep learning architecture that combines those features and apply it to the probabilistic and binary classification task for all English sets and Spanish. We achieved reasonable performance on all sets with best performances seen on the probabilistic task, particularly on the English news set (MAE 0.054 and F1-score of 0.872). An analysis of the results shows that reasonable performance can be achieved with a single architecture without any domain-specific tweaking of the parameter settings and that distributional features capture almost all of the information also found in hand-crafted features.</abstract>
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    <identifier type="doi">10.18653/v1/W18-0539</identifier>
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        <date>2018-06</date>
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%0 Conference Proceedings
%T Deep Learning Architecture for Complex Word Identification
%A De Hertog, Dirk
%A Tack, Anaïs
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F de-hertog-tack-2018-deep
%X We describe a system for the CWI-task that includes information on 5 aspects of the (complex) lexical item, namely distributional information of the item itself, morphological structure, psychological measures, corpus-counts and topical information. We constructed a deep learning architecture that combines those features and apply it to the probabilistic and binary classification task for all English sets and Spanish. We achieved reasonable performance on all sets with best performances seen on the probabilistic task, particularly on the English news set (MAE 0.054 and F1-score of 0.872). An analysis of the results shows that reasonable performance can be achieved with a single architecture without any domain-specific tweaking of the parameter settings and that distributional features capture almost all of the information also found in hand-crafted features.
%R 10.18653/v1/W18-0539
%U https://aclanthology.org/W18-0539
%U https://doi.org/10.18653/v1/W18-0539
%P 328-334

Markdown (Informal)

[Deep Learning Architecture for Complex Word Identification](https://aclanthology.org/W18-0539) (De Hertog & Tack, BEA 2018)

• Deep Learning Architecture for Complex Word Identification (De Hertog & Tack, BEA 2018)

ACL

• Dirk De Hertog and Anaïs Tack. 2018. Deep Learning Architecture for Complex Word Identification. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 328–334, New Orleans, Louisiana. Association for Computational Linguistics.