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.

Universal Semantic Representation (USR) is designed as a language-independent information packaging system that captures information at three levels: (a) Lexico-conceptual, (b) Syntactico-Semantic, and (c) Discourse. Unlike other representations that mainly encode predicates and their argument structures, our proposed representation captures the speaker’s vivakṣā- how the speaker views the activity. The idea of “speaker’s vivakṣā is inspired by Indian Grammatical Tradition. There can be some amount of idiosyncrasy of the speaker in the annotation since it is the speaker’s view- point that has been captured in the annotation. Hence the evaluation metrics of such resources need to be also thought through from scratch. This paper presents an extensive evaluation procedure of this semantic representation from two perspectives (a) Inter- Annotator Agreement and (b) one downstream task, namely multilingual Natural Language Generation. We also qualitatively evaluate the experience of natural language generation by manual parsing of USR, so as to understand the readability of USR. We have achieved above 80% Inter-Annotator Agreement for USR annotations and above 80% semantic closeness in multi-lingual generation tasks suggesting the reliability of USR annotations and utility for multi-lingual generations. The qualitative evaluation also suggests high readability and hence the utility of USR as a semantic representation.

This paper examines widely prevalent yet little-studied expressions in Indian languages which are known as geometrical terms be-cause “they engage locations along the axes of the reference object”. These terms are andara (inside), b ̄ahara (outside), ̄age (in front of), s ̄amane (in front of), p ̄ıche (back), ̄upara (above/over), n ̄ıce (under/below), d ̄ayem. (right), b ̄ayem. (left), p ̄asa (near), d ̄ura (away/far) in Hindi. The way these terms have been interpreted by the scholars of the Hindi language and handled in the Hindi Dependency treebank is misleading. This paper proposes an alternative analysis of these terms focusing on their triple – nominal, modifier and relational - functions and presents abstract semantic representations of these terms following the proposed analysis. The semantic representation will be explicit, unambiguous abstract and therefore universal in nature. The correspondence of these terms in Bangla and Kannada are also identified. Disambiguation of geometric terms will facilitate parsing and machine translation especially from Indian Language to English because these geometric terms of Indian languages are variedly translated in English de-pending on context.