Several studies have used distant supervision to create resources for fine-grained entity recognition (FgER) to mitigate the challenges of manual annotation.However, most of these methods are primarily developed for English and cannot be efficiently adapted to many other languages, including Indian languages.Moreover, the emergence of new and unseen entity types deteriorates the performance of the supervised models trained on FgER datasets with different predefined sets of entity types.This work introduces TAFSIL, a taxonomy-adaptable FgER framework to create FgER datasets in six Indian languages.The chosen languages are spoken by more than a billion speakers across various countries.TAFSIL utilizes the high interlink between the knowledge base WikiData and linked corpora Wikipedia through multi-stage heuristics and improves annotation through fuzzy match and quality sentence selection.TAFSIL enables us to create datasets of a total size of around three million samples for six languages Hindi (Hi), Marathi (Mr), Sanskrit (Sa), Tamil (Ta), Telugu (Te), and Urdu (Ur) belonging to two language families Indo-European and Dravidian.We evaluate the robustness of TAFSIL by creating various datasets in four taxonomies FIGER, OntoNotes, HAnDS, and MultiCoNER2.Our extensive experiments suggest the sound quality of the datasets as there is a relative improvement of 83% in average F1 score over zero-shot performance across various FgER state-of-the-art models.The resource is publicly available at https://huggingface.co/datasets/prachuryyaI- ITG/TAFSIL.

The growing popularity of online social networks is quite evident nowadays and provides an opportunity to allow researchers in finding solutions for various practical applications. Link prediction is the technique of understanding network structure and identifying missing and future links in social networks. One of the well-known classes of methods in link prediction is a similarity-based method, which uses local and global topological information of the network to predict missing links. Some methods also exist based on quasi-local features to achieve a trade-off between local and global information on static networks. These quasi-local similarity-based methods are not best suited for considering community information in dynamic networks, failing to balance accuracy and efficiency. Therefore, a community-enhanced framework is presented in this article to predict missing links on dynamic social networks. First, a link prediction framework is presented to predict missing links using parameterized influence regions of nodes and their contribution in community partitions. Then, a unique feature set is generated using local, global, and quasi-local similarity-based as well as community information-based features. This feature set is further optimized using scoring-based feature selection methods to select only the most relevant features. Finally, four machine learning-based classification models are used for link prediction. The experiments are performed on six well-known dynamic networks and three performance metrics, and the results demonstrate that the proposed method outperforms the state-of-the-art methods.