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        <title>Revisiting Anwesha:Enhancing Personalised and Natural Search in Bangla</title>
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        <namePart type="given">Arup</namePart>
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        <namePart type="given">Joyojyoti</namePart>
        <namePart type="family">Acharya</namePart>
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        <namePart type="given">Bibekananda</namePart>
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        <namePart type="given">Sutanu</namePart>
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            <namePart type="given">Shad</namePart>
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            <namePart type="given">Tanmoy</namePart>
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    <abstract>Bangla is a low-resource, highly agglutinative language. Thus it is challenging to facilitate an effective search of Bangla documents. We have created a gold standard dataset containing query document relevance pairs for evaluation purposes. We utilise Named Entities to improve the retrieval effectiveness of traditional Bangla search algorithms. We suggest a reasonable starting model for leveraging implicit preference feedback based on the user search behaviour to enhance the results retrieved by the Explicit Semantic Analysis (ESA) approach. We use contextual sentence embeddings obtained via Language-agnostic BERT Sentence Embedding (LaBSE) to rerank the candidate documents retrieved by the traditional search algorithms (tf-idf) based on the top sentences that are most relevant to the query. This paper presents our empirical findings across these directions and critically analyses the results.</abstract>
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%0 Conference Proceedings
%T Revisiting Anwesha:Enhancing Personalised and Natural Search in Bangla
%A Das, Arup
%A Acharya, Joyojyoti
%A Kundu, Bibekananda
%A Chakraborti, Sutanu
%Y Akhtar, Md. Shad
%Y Chakraborty, Tanmoy
%S Proceedings of the 19th International Conference on Natural Language Processing (ICON)
%D 2022
%8 December
%I Association for Computational Linguistics
%C New Delhi, India
%F das-etal-2022-revisiting
%X Bangla is a low-resource, highly agglutinative language. Thus it is challenging to facilitate an effective search of Bangla documents. We have created a gold standard dataset containing query document relevance pairs for evaluation purposes. We utilise Named Entities to improve the retrieval effectiveness of traditional Bangla search algorithms. We suggest a reasonable starting model for leveraging implicit preference feedback based on the user search behaviour to enhance the results retrieved by the Explicit Semantic Analysis (ESA) approach. We use contextual sentence embeddings obtained via Language-agnostic BERT Sentence Embedding (LaBSE) to rerank the candidate documents retrieved by the traditional search algorithms (tf-idf) based on the top sentences that are most relevant to the query. This paper presents our empirical findings across these directions and critically analyses the results.
%U https://aclanthology.org/2022.icon-main.24
%P 183-193

Markdown (Informal)

[Revisiting Anwesha:Enhancing Personalised and Natural Search in Bangla](https://aclanthology.org/2022.icon-main.24) (Das et al., ICON 2022)

• Revisiting Anwesha:Enhancing Personalised and Natural Search in Bangla (Das et al., ICON 2022)

ACL

• Arup Das, Joyojyoti Acharya, Bibekananda Kundu, and Sutanu Chakraborti. 2022. Revisiting Anwesha:Enhancing Personalised and Natural Search in Bangla. In Proceedings of the 19th International Conference on Natural Language Processing (ICON), pages 183–193, New Delhi, India. Association for Computational Linguistics.