Anagha Kulkarni
2026
Long-Context Long-Form Question Answering for Legal Domain
Anagha Kulkarni | Parin Rajesh Jhaveri | Prasha Shrestha | Yu Tong Han | Reza Amini | Behrouz Madahian
Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)
Anagha Kulkarni | Parin Rajesh Jhaveri | Prasha Shrestha | Yu Tong Han | Reza Amini | Behrouz Madahian
Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)
Legal documents have complex document layouts involving multiple nested sections, lengthy footnotes and further use specialized linguistic devices like intricate syntax and domain-specific vocabulary to ensure precision and authority. These inherent characteristics of legal documents make question answering challenging, and particularly so when the answer to the question spans several pages (i.e. requires long-context) and is required to be comprehensive (i.e. a long-form answer).In this paper, we address the challenges of long-context question answering in context of long-form answers given the idiosyncrasies of legal documents. We propose a question answering system that can (a) deconstruct domain-specific vocabulary for better retrieval from source documents, (b) parse complex document layouts while isolating sections and footnotes and linking them appropriately, (c) generate comprehensive answers using precise domain-specific vocabulary. We also introduce a coverage metric that classifies the performance into recall-based coverage categories allowing human users to evaluate the recall with ease. By leveraging the expertise of professionals from fields such as law and corporate tax, we curate a QA dataset. Through comprehensive experiments and ablation studies, we demonstrate the usability and merit of the proposed system.
2008
Dictionary Definitions based Homograph Identification using a Generative Hierarchical Model
Anagha Kulkarni | Jamie Callan
Proceedings of ACL-08: HLT, Short Papers
Anagha Kulkarni | Jamie Callan
Proceedings of ACL-08: HLT, Short Papers
2006
Selecting the “Right” Number of Senses Based on Clustering Criterion Functions
Ted Pedersen | Anagha Kulkarni
Demonstrations
Ted Pedersen | Anagha Kulkarni
Demonstrations
Automatic Cluster Stopping with Criterion Functions and the Gap Statistic
Ted Pedersen | Anagha Kulkarni
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Demonstrations
Ted Pedersen | Anagha Kulkarni
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Demonstrations
Improving Name Discrimination: A Language Salad Approach
Ted Pedersen | Anagha Kulkarni | Roxana Angheluta | Zornitsa Kozareva | Thamar Solorio
Proceedings of the Cross-Language Knowledge Induction Workshop
Ted Pedersen | Anagha Kulkarni | Roxana Angheluta | Zornitsa Kozareva | Thamar Solorio
Proceedings of the Cross-Language Knowledge Induction Workshop