@inproceedings{paul-etal-2025-indian,
title = "{I}ndian Grammatical Tradition-Inspired Universal Semantic Representation Bank ({USR} Bank 1.0)",
author = "Paul, Soma and
Sukhada, Sukhada and
Bhattacharjee, Bidisha and
Riya, Kumari and
Tatavolu, Sashank and
R, Kamesh and
Anwar, Isma and
Rani, Pratibha",
editor = "Bhattacharya, Arnab and
Goyal, Pawan and
Ghosh, Saptarshi and
Ghosh, Kripabandhu",
booktitle = "Proceedings of the 1st Workshop on Benchmarks, Harmonization, Annotation, and Standardization for Human-Centric AI in Indian Languages (BHASHA 2025)",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.bhasha-1.2/",
pages = "11--22",
ISBN = "979-8-89176-313-5",
abstract = "In this paper, we introduce USR Bank 1.0, a multi-layered, text-level semantic representation framework designed to capture not only the predicate-argument structure of an utterance but also the speaker{'}s communicative intent as expressed linguistically. Built on the Universal Semantic Grammar (USG), which is grounded in P{\={a}}ṇinian grammar and the Indian Grammatical Tradition (IGT), USR systematically encodes semantic, morpho-syntactic, discourse, and pragmatic information across distinct layers. In the USR generation process, initial USRs are automatically generated using a dedicated USR-builder tool and subsequently validated via a web-based interface (SAVI), ensuring high inter-annotator agreement and semantic fidelity. Our evaluation on Hindi texts demonstrates robust dependency and discourse annotation consistency and strong semantic similarity in USR-to-text generation. By distributing semantic-pragmatic information across layers and capturing the speaker{'}s perspective, USR provides a cognitively motivated, language-agnostic framework with promising applications in multilingual natural language processing."
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<abstract>In this paper, we introduce USR Bank 1.0, a multi-layered, text-level semantic representation framework designed to capture not only the predicate-argument structure of an utterance but also the speaker’s communicative intent as expressed linguistically. Built on the Universal Semantic Grammar (USG), which is grounded in Pāṇinian grammar and the Indian Grammatical Tradition (IGT), USR systematically encodes semantic, morpho-syntactic, discourse, and pragmatic information across distinct layers. In the USR generation process, initial USRs are automatically generated using a dedicated USR-builder tool and subsequently validated via a web-based interface (SAVI), ensuring high inter-annotator agreement and semantic fidelity. Our evaluation on Hindi texts demonstrates robust dependency and discourse annotation consistency and strong semantic similarity in USR-to-text generation. By distributing semantic-pragmatic information across layers and capturing the speaker’s perspective, USR provides a cognitively motivated, language-agnostic framework with promising applications in multilingual natural language processing.</abstract>
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%0 Conference Proceedings
%T Indian Grammatical Tradition-Inspired Universal Semantic Representation Bank (USR Bank 1.0)
%A Paul, Soma
%A Sukhada, Sukhada
%A Bhattacharjee, Bidisha
%A Riya, Kumari
%A Tatavolu, Sashank
%A R, Kamesh
%A Anwar, Isma
%A Rani, Pratibha
%Y Bhattacharya, Arnab
%Y Goyal, Pawan
%Y Ghosh, Saptarshi
%Y Ghosh, Kripabandhu
%S Proceedings of the 1st Workshop on Benchmarks, Harmonization, Annotation, and Standardization for Human-Centric AI in Indian Languages (BHASHA 2025)
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-313-5
%F paul-etal-2025-indian
%X In this paper, we introduce USR Bank 1.0, a multi-layered, text-level semantic representation framework designed to capture not only the predicate-argument structure of an utterance but also the speaker’s communicative intent as expressed linguistically. Built on the Universal Semantic Grammar (USG), which is grounded in Pāṇinian grammar and the Indian Grammatical Tradition (IGT), USR systematically encodes semantic, morpho-syntactic, discourse, and pragmatic information across distinct layers. In the USR generation process, initial USRs are automatically generated using a dedicated USR-builder tool and subsequently validated via a web-based interface (SAVI), ensuring high inter-annotator agreement and semantic fidelity. Our evaluation on Hindi texts demonstrates robust dependency and discourse annotation consistency and strong semantic similarity in USR-to-text generation. By distributing semantic-pragmatic information across layers and capturing the speaker’s perspective, USR provides a cognitively motivated, language-agnostic framework with promising applications in multilingual natural language processing.
%U https://aclanthology.org/2025.bhasha-1.2/
%P 11-22
Markdown (Informal)
[Indian Grammatical Tradition-Inspired Universal Semantic Representation Bank (USR Bank 1.0)](https://aclanthology.org/2025.bhasha-1.2/) (Paul et al., BHASHA 2025)
ACL
- Soma Paul, Sukhada Sukhada, Bidisha Bhattacharjee, Kumari Riya, Sashank Tatavolu, Kamesh R, Isma Anwar, and Pratibha Rani. 2025. Indian Grammatical Tradition-Inspired Universal Semantic Representation Bank (USR Bank 1.0). In Proceedings of the 1st Workshop on Benchmarks, Harmonization, Annotation, and Standardization for Human-Centric AI in Indian Languages (BHASHA 2025), pages 11–22, Mumbai, India. Association for Computational Linguistics.