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.

We introduce a generic, human-out-of-the-loop pipeline, ERLKG, to perform rapid association analysis of any biomedical entity with other existing entities from a corpora of the same domain. Our pipeline consists of a Knowledge Graph (KG) created from the Open Source CORD-19 dataset by fully automating the procedure of information extraction using SciBERT. The best latent entity representations are then found by benchnmarking different KG embedding techniques on the task of link prediction using a Graph Convolution Network Auto Encoder (GCN-AE). We demonstrate the utility of ERLKG with respect to COVID-19 through multiple qualitative evaluations. Due to the lack of a gold standard, we propose a relatively large intrinsic evaluation dataset for COVID-19 and use it for validating the top two performing KG embedding techniques. We find TransD to be the best performing KG embedding technique with Pearson and Spearman correlation scores of 0.4348 and 0.4570 respectively. We demonstrate that a considerable number of ERLKG’s top protein, chemical and disease predictions are currently in consideration for COVID-19 related research.

Relation classification is crucial for inferring semantic relatedness between entities in a piece of text. These systems can be trained given labelled data. However, relation classification is very domain-specific and it takes a lot of effort to label data for a new domain. In this paper, we explore domain adaptation techniques for this task. While past works have focused on single source domain adaptation for bio-medical relation classification, we classify relations in an unlabeled target domain by transferring useful knowledge from one or more related source domains. Our experiments with the model have shown to improve state-of-the-art F1 score on 3 benchmark biomedical corpora for single domain and on 2 out of 3 for multi-domain scenarios. When used with contextualized embeddings, there is further boost in performance outperforming neural-network based domain adaptation baselines for both the cases.