Sanskrit multi-component compounding presents a severe structured prediction challenge: the space of valid parse structures grows exponentially with compound length, implicit semantic relations are highly context-sensitive, and accurate type assignment demands simultaneous global structural coherence and local morphological precision across a fine-grained label typology. Existing systems address Nested Compound Type Identification for Sanskrit (NeCTIS) discriminatively, predicting dependency edges and semantic types in a single greedy pass. Reformulating NeCTIS as iterative generative refinement via diffusion over the full label sequence resolves this structural inconsistency but exposes a complementary bottleneck - coarse global attention that suppresses the localized morphological signals necessary for fine-grained type discrimination. We address this through a local refinement mechanism within a restricted window.

We further provide the first systematic evaluation of large language models on NeCTIS, finding consistent and substantial failures under zero-shot and few-shot conditions - attributable to the absence of structural inductive biases, Sanskrit under-representation in pretraining, and morphological sensitivity requirements that broad-coverage models are fundamentally ill-equipped to satisfy.

Multi-component compounding is a prevalent phenomenon in Sanskrit, and understanding the implicit structure of a compound’s components is crucial for deciphering its meaning. Earlier approaches in Sanskrit have focused on binary compounds and neglected the multi-component compound setting. This work introduces the novel task of nested compound type identification (NeCTI), which aims to identify nested spans of a multi-component compound and decode the implicit semantic relations between them. To the best of our knowledge, this is the first attempt in the field of lexical semantics to propose this task. We present 2 newly annotated datasets including an out-of-domain dataset for this task. We also benchmark these datasets by exploring the efficacy of the standard problem formulations such as nested named entity recognition, constituency parsing and seq2seq, etc. We present a novel framework named DepNeCTI: Dependency-based Nested Compound Type Identifier that surpasses the performance of the best baseline with an average absolute improvement of 13.1 points F1-score in terms of Labeled Span Score (LSS) and a 5-fold enhancement in inference efficiency. In line with the previous findings in the binary Sanskrit compound identification task, context provides benefits for the NeCTI task. The codebase and datasets are publicly available at: https://github.com/yaswanth-iitkgp/DepNeCTI

Computationally analyzing Sanskrit texts requires proper segmentation in the initial stages. There have been various tools developed for Sanskrit text segmentation. Of these, Gérard Huet’s Reader in the Sanskrit Heritage Engine analyzes the input text and segments it based on the word parameters - phases like iic, ifc, Pr, Subst, etc., and sandhi (or transition) that takes place at the end of a word with the initial part of the next word. And it enlists all the possible solutions differentiating them with the help of the phases. The phases and their analyses have their use in the domain of sentential parsers. In segmentation, though, they are not used beyond deciding whether the words formed with the phases are morphologically valid. This paper tries to modify the above segmenter by ignoring the phase details (except for a few cases), and also proposes a probability function to prioritize the list of solutions to bring up the most valid solutions at the top.