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        <title>iNLTK: Natural Language Toolkit for Indic Languages</title>
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        <namePart type="given">Gaurav</namePart>
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            <namePart type="given">Eunjeong</namePart>
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        <name type="personal">
            <namePart type="given">Masato</namePart>
            <namePart type="family">Hagiwara</namePart>
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        <name type="personal">
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            <namePart type="family">Chauhan</namePart>
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            <namePart type="given">Liling</namePart>
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    <abstract>We present iNLTK, an open-source NLP library consisting of pre-trained language models and out-of-the-box support for Data Augmentation, Textual Similarity, Sentence Embeddings, Word Embeddings, Tokenization and Text Generation in 13 Indic Languages. By using pre-trained models from iNLTK for text classification on publicly available datasets, we significantly outperform previously reported results. On these datasets, we also show that by using pre-trained models and data augmentation from iNLTK, we can achieve more than 95% of the previous best performance by using less than 10% of the training data. iNLTK is already being widely used by the community and has 40,000+ downloads, 600+ stars and 100+ forks on GitHub. The library is available at https://github.com/goru001/inltk.</abstract>
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    <identifier type="doi">10.18653/v1/2020.nlposs-1.10</identifier>
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%0 Conference Proceedings
%T iNLTK: Natural Language Toolkit for Indic Languages
%A Arora, Gaurav
%Y Park, Eunjeong L.
%Y Hagiwara, Masato
%Y Milajevs, Dmitrijs
%Y Liu, Nelson F.
%Y Chauhan, Geeticka
%Y Tan, Liling
%S Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F arora-2020-inltk
%X We present iNLTK, an open-source NLP library consisting of pre-trained language models and out-of-the-box support for Data Augmentation, Textual Similarity, Sentence Embeddings, Word Embeddings, Tokenization and Text Generation in 13 Indic Languages. By using pre-trained models from iNLTK for text classification on publicly available datasets, we significantly outperform previously reported results. On these datasets, we also show that by using pre-trained models and data augmentation from iNLTK, we can achieve more than 95% of the previous best performance by using less than 10% of the training data. iNLTK is already being widely used by the community and has 40,000+ downloads, 600+ stars and 100+ forks on GitHub. The library is available at https://github.com/goru001/inltk.
%R 10.18653/v1/2020.nlposs-1.10
%U https://aclanthology.org/2020.nlposs-1.10
%U https://doi.org/10.18653/v1/2020.nlposs-1.10
%P 66-71

Markdown (Informal)

[iNLTK: Natural Language Toolkit for Indic Languages](https://aclanthology.org/2020.nlposs-1.10) (Arora, NLPOSS 2020)

• iNLTK: Natural Language Toolkit for Indic Languages (Arora, NLPOSS 2020)

ACL

• Gaurav Arora. 2020. iNLTK: Natural Language Toolkit for Indic Languages. In Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS), pages 66–71, Online. Association for Computational Linguistics.