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        <title>Embedding-based Scientific Literature Discovery in a Text Editor Application</title>
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    <name type="personal">
        <namePart type="given">Nianlong</namePart>
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    <abstract>Each claim in a research paper requires all relevant prior knowledge to be discovered, assimilated, and appropriately cited. However, despite the availability of powerful search engines and sophisticated text editing software, discovering relevant papers and integrating the knowledge into a manuscript remain complex tasks associated with high cognitive load. To define comprehensive search queries requires strong motivation from authors, irrespective of their familiarity with the research field. Moreover, switching between independent applications for literature discovery, bibliography management, reading papers, and writing text burdens authors further and interrupts their creative process. Here, we present a web application that combines text editing and literature discovery in an interactive user interface. The application is equipped with a search engine that couples Boolean keyword filtering with nearest neighbor search over text embeddings, providing a discovery experience tuned to an author’s manuscript and his interests. Our application aims to take a step towards more enjoyable and effortless academic writing. The demo of the application (https://SciEditorDemo2020.herokuapp.com) and a short video tutorial (https://youtu.be/pkdVU60IcRc) are available online.</abstract>
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    <identifier type="doi">10.18653/v1/2020.acl-demos.36</identifier>
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        <date>2020-07</date>
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%0 Conference Proceedings
%T Embedding-based Scientific Literature Discovery in a Text Editor Application
%A Gökçe, Onur
%A Prada, Jonathan
%A Nikolov, Nikola I.
%A Gu, Nianlong
%A Hahnloser, Richard H.R.
%Y Celikyilmaz, Asli
%Y Wen, Tsung-Hsien
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F gokce-etal-2020-embedding
%X Each claim in a research paper requires all relevant prior knowledge to be discovered, assimilated, and appropriately cited. However, despite the availability of powerful search engines and sophisticated text editing software, discovering relevant papers and integrating the knowledge into a manuscript remain complex tasks associated with high cognitive load. To define comprehensive search queries requires strong motivation from authors, irrespective of their familiarity with the research field. Moreover, switching between independent applications for literature discovery, bibliography management, reading papers, and writing text burdens authors further and interrupts their creative process. Here, we present a web application that combines text editing and literature discovery in an interactive user interface. The application is equipped with a search engine that couples Boolean keyword filtering with nearest neighbor search over text embeddings, providing a discovery experience tuned to an author’s manuscript and his interests. Our application aims to take a step towards more enjoyable and effortless academic writing. The demo of the application (https://SciEditorDemo2020.herokuapp.com) and a short video tutorial (https://youtu.be/pkdVU60IcRc) are available online.
%R 10.18653/v1/2020.acl-demos.36
%U https://aclanthology.org/2020.acl-demos.36
%U https://doi.org/10.18653/v1/2020.acl-demos.36
%P 320-326

Markdown (Informal)

[Embedding-based Scientific Literature Discovery in a Text Editor Application](https://aclanthology.org/2020.acl-demos.36) (Gökçe et al., ACL 2020)

• Embedding-based Scientific Literature Discovery in a Text Editor Application (Gökçe et al., ACL 2020)

ACL

• Onur Gökçe, Jonathan Prada, Nikola I. Nikolov, Nianlong Gu, and Richard H.R. Hahnloser. 2020. Embedding-based Scientific Literature Discovery in a Text Editor Application. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 320–326, Online. Association for Computational Linguistics.