Yashodhara Haribhakta


2025

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BERTopic for Topic Modeling of Hindi Short Texts: A Comparative Study
Atharva Mutsaddi | Anvi Jamkhande | Aryan Shirish Thakre | Yashodhara Haribhakta
Proceedings of the First Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages

As short text data in native languages like Hindi increasingly appear in modern media, robust methods for topic modeling on such data have gained importance. This study investigates the performance of BERTopic in modeling Hindi short texts, an area that has been under-explored in existing research. Using contextual embeddings, BERTopic can capture semantic relationships in data, making it potentially more effective than traditional models, especially for short and diverse texts. We evaluate BERTopic using 6 different document embedding models and compare its performance against 8 established topic modeling techniques, such as Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), Latent Semantic Indexing (LSI), Additive Regularization of Topic Models (ARTM), Probabilistic Latent Semantic Analysis (PLSA), Embedded Topic Model (ETM), Combined Topic Model (CTM), and Top2Vec. The models are assessed using coherence scores across a range of topic counts. Our results reveal that BERTopic consistently outperforms other models in capturing coherent topics from short Hindi texts.

2023

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ArgAnalysis35K : A large-scale dataset for Argument Quality Analysis
Omkar Joshi | Priya Pitre | Yashodhara Haribhakta
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Argument Quality Detection is an emerging field in NLP which has seen significant recent development. However, existing datasets in this field suffer from a lack of quality, quantity and diversity of topics and arguments, specifically the presence of vague arguments that are not persuasive in nature. In this paper, we leverage a combined experience of 10+ years of Parliamentary Debating to create a dataset that covers significantly more topics and has a wide range of sources to capture more diversity of opinion. With 34,890 high-quality argument-analysis pairs (a term we introduce in this paper), this is also the largest dataset of its kind to our knowledge. In addition to this contribution, we introduce an innovative argument scoring system based on instance-level annotator reliability and propose a quantitative model of scoring the relevance of arguments to a range of topics.

2017

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nQuery - A Natural Language Statement to SQL Query Generator
Nandan Sukthankar | Sanket Maharnawar | Pranay Deshmukh | Yashodhara Haribhakta | Vibhavari Kamble
Proceedings of ACL 2017, Student Research Workshop