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        <title>Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem</title>
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    <name type="personal">
        <namePart type="given">Ryoma</namePart>
        <namePart type="family">Sato</namePart>
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            <title>Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies</title>
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        <name type="personal">
            <namePart type="given">Marine</namePart>
            <namePart type="family">Carpuat</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Marie-Catherine</namePart>
            <namePart type="family">de Marneffe</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Ivan</namePart>
            <namePart type="given">Vladimir</namePart>
            <namePart type="family">Meza Ruiz</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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    <abstract>Word embeddings are one of the most fundamental technologies used in natural language processing. Existing word embeddings are high-dimensional and consume considerable computational resources. In this study, we propose WordTour, unsupervised one-dimensional word embeddings. To achieve the challenging goal, we propose a decomposition of the desiderata of word embeddings into two parts, completeness and soundness, and focus on soundness in this paper. Owing to the single dimensionality, WordTour is extremely efficient and provides a minimal means to handle word embeddings. We experimentally confirmed the effectiveness of the proposed method via user study and document classification.</abstract>
    <identifier type="citekey">sato-2022-word</identifier>
    <identifier type="doi">10.18653/v1/2022.naacl-main.157</identifier>
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            <start>2166</start>
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%0 Conference Proceedings
%T Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem
%A Sato, Ryoma
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F sato-2022-word
%X Word embeddings are one of the most fundamental technologies used in natural language processing. Existing word embeddings are high-dimensional and consume considerable computational resources. In this study, we propose WordTour, unsupervised one-dimensional word embeddings. To achieve the challenging goal, we propose a decomposition of the desiderata of word embeddings into two parts, completeness and soundness, and focus on soundness in this paper. Owing to the single dimensionality, WordTour is extremely efficient and provides a minimal means to handle word embeddings. We experimentally confirmed the effectiveness of the proposed method via user study and document classification.
%R 10.18653/v1/2022.naacl-main.157
%U https://aclanthology.org/2022.naacl-main.157
%U https://doi.org/10.18653/v1/2022.naacl-main.157
%P 2166-2172

Markdown (Informal)

[Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem](https://aclanthology.org/2022.naacl-main.157) (Sato, NAACL 2022)

• Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem (Sato, NAACL 2022)

ACL

• Ryoma Sato. 2022. Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2166–2172, Seattle, United States. Association for Computational Linguistics.