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    <titleInfo>
        <title>Content Explorer: Recommending Novel Entities for a Document Writer</title>
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        <namePart type="given">Michal</namePart>
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        <namePart type="given">Richard</namePart>
        <namePart type="family">Zens</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
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    <abstract>Background research is an essential part of document writing. Search engines are great for retrieving information once we know what to look for. However, the bigger challenge is often identifying topics for further research. Automated tools could help significantly in this discovery process and increase the productivity of the writer. In this paper, we formulate the problem of recommending topics to a writer. We consider this as a supervised learning problem and run a user study to validate this approach. We propose an evaluation metric and perform an empirical comparison of state-of-the-art models for extreme multi-label classification on a large data set. We demonstrate how a simple modification of the cross-entropy loss function leads to improved results of the deep learning models.</abstract>
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        <date>2018-oct-nov</date>
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            <start>3371</start>
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%0 Conference Proceedings
%T Content Explorer: Recommending Novel Entities for a Document Writer
%A Lukasik, Michal
%A Zens, Richard
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F lukasik-zens-2018-content
%X Background research is an essential part of document writing. Search engines are great for retrieving information once we know what to look for. However, the bigger challenge is often identifying topics for further research. Automated tools could help significantly in this discovery process and increase the productivity of the writer. In this paper, we formulate the problem of recommending topics to a writer. We consider this as a supervised learning problem and run a user study to validate this approach. We propose an evaluation metric and perform an empirical comparison of state-of-the-art models for extreme multi-label classification on a large data set. We demonstrate how a simple modification of the cross-entropy loss function leads to improved results of the deep learning models.
%R 10.18653/v1/D18-1374
%U https://aclanthology.org/D18-1374
%U https://doi.org/10.18653/v1/D18-1374
%P 3371-3380

Markdown (Informal)

[Content Explorer: Recommending Novel Entities for a Document Writer](https://aclanthology.org/D18-1374) (Lukasik & Zens, EMNLP 2018)

• Content Explorer: Recommending Novel Entities for a Document Writer (Lukasik & Zens, EMNLP 2018)

ACL

• Michal Lukasik and Richard Zens. 2018. Content Explorer: Recommending Novel Entities for a Document Writer. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3371–3380, Brussels, Belgium. Association for Computational Linguistics.