@inproceedings{sarkar-etal-2025-mahanama,
    title = "Mah{\={a}}n{\={a}}ma: A Unique Testbed for Literary Entity Discovery and Linking",
    author = "Sarkar, Sujoy  and
      Sarkar, Gourav  and
      Jagadeeshan, Manoj Balaji  and
      Sandhan, Jivnesh  and
      Krishna, Amrith  and
      Goyal, Pawan",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1269/",
    pages = "24981--24995",
    ISBN = "979-8-89176-332-6",
    abstract = "High lexical variation, ambiguous references, and long-range dependencies make entity resolution in literary texts particularly challenging. We present Mah{\={a}}n{\={a}}ma, the first large-scale dataset for end-to-end Entity Discovery and Linking (EDL) in Sanskrit, a morphologically rich and under-resourced language. Derived from the Mah{\={a}}bh{\={a}}rata , the world{'}s longest epic, the dataset comprises over 109K named entity mentions mapped to 5.5K unique entities, and is aligned with an English knowledge base to support cross-lingual linking. The complex narrative structure of Mah{\={a}}n{\={a}}ma, coupled with extensive name variation and ambiguity, poses significant challenges to resolution systems. Our evaluation reveals that current coreference and entity linking models struggle when evaluated on the global context of the test set. These results highlight the limitations of current approaches in resolving entities within such complex discourse. Mah{\={a}}n{\={a}}ma thus provides a unique benchmark for advancing entity resolution, especially in literary domains."
}