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        <title>Word Similarity Datasets for Indian Languages: Annotation and Baseline Systems</title>
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    <name type="personal">
        <namePart type="given">Syed</namePart>
        <namePart type="given">Sarfaraz</namePart>
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    <name type="personal">
        <namePart type="given">Arihant</namePart>
        <namePart type="family">Gupta</namePart>
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    <name type="personal">
        <namePart type="given">Avijit</namePart>
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    <name type="personal">
        <namePart type="given">Arjit</namePart>
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    <name type="personal">
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            <title>Proceedings of the 11th Linguistic Annotation Workshop</title>
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            <namePart type="given">Nathan</namePart>
            <namePart type="family">Schneider</namePart>
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    <abstract>With the advent of word representations, word similarity tasks are becoming increasing popular as an evaluation metric for the quality of the representations. In this paper, we present manually annotated monolingual word similarity datasets of six Indian languages - Urdu, Telugu, Marathi, Punjabi, Tamil and Gujarati. These languages are most spoken Indian languages worldwide after Hindi and Bengali. For the construction of these datasets, our approach relies on translation and re-annotation of word similarity datasets of English. We also present baseline scores for word representation models using state-of-the-art techniques for Urdu, Telugu and Marathi by evaluating them on newly created word similarity datasets.</abstract>
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%0 Conference Proceedings
%T Word Similarity Datasets for Indian Languages: Annotation and Baseline Systems
%A Akhtar, Syed Sarfaraz
%A Gupta, Arihant
%A Vajpayee, Avijit
%A Srivastava, Arjit
%A Shrivastava, Manish
%Y Schneider, Nathan
%Y Xue, Nianwen
%S Proceedings of the 11th Linguistic Annotation Workshop
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F akhtar-etal-2017-word
%X With the advent of word representations, word similarity tasks are becoming increasing popular as an evaluation metric for the quality of the representations. In this paper, we present manually annotated monolingual word similarity datasets of six Indian languages - Urdu, Telugu, Marathi, Punjabi, Tamil and Gujarati. These languages are most spoken Indian languages worldwide after Hindi and Bengali. For the construction of these datasets, our approach relies on translation and re-annotation of word similarity datasets of English. We also present baseline scores for word representation models using state-of-the-art techniques for Urdu, Telugu and Marathi by evaluating them on newly created word similarity datasets.
%R 10.18653/v1/W17-0811
%U https://aclanthology.org/W17-0811
%U https://doi.org/10.18653/v1/W17-0811
%P 91-94

Markdown (Informal)

[Word Similarity Datasets for Indian Languages: Annotation and Baseline Systems](https://aclanthology.org/W17-0811) (Akhtar et al., LAW 2017)

• Word Similarity Datasets for Indian Languages: Annotation and Baseline Systems (Akhtar et al., LAW 2017)

ACL

• Syed Sarfaraz Akhtar, Arihant Gupta, Avijit Vajpayee, Arjit Srivastava, and Manish Shrivastava. 2017. Word Similarity Datasets for Indian Languages: Annotation and Baseline Systems. In Proceedings of the 11th Linguistic Annotation Workshop, pages 91–94, Valencia, Spain. Association for Computational Linguistics.