Mahānāma: A Unique Testbed for Literary Entity Discovery and Linking

• URL: http://arxiv.org/abs/2509.19844v1
• Date: Wed, 24 Sep 2025 07:42:39 GMT
• Title: Mahānāma: A Unique Testbed for Literary Entity Discovery and Linking
• Abstract summary: Mah=an=ama is the first large-scale dataset for end-to-end Entity Discovery and Linking in Sanskrit.<n>The dataset comprises over 109K named entity mentions mapped to 5.5K unique entities.<n>The complex narrative structure of Mah=an=ama, coupled with extensive name variation and ambiguity, poses significant challenges to resolution systems.
• Score: 5.247057581790816
• License: http://creativecommons.org/licenses/by/4.0/
• 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\=an\=ama thus provides a unique benchmark for advancing entity resolution, especially in literary domains.

Related papers

• Structure-Guided Entity Resolution: Fine-Tuning LLMs for Robust Name Matching in Complex Linguistic Contexts [0.0]
  This paper introduces Structure-Guided Entity Resolution (SGER), a novel framework that fine-tunes an Large Language Model through a two-phase curriculum.<n>We evaluate SGER in the challenging context of Indian identity data, one of the most linguistically diverse and noisy environments globally.<n>SGER achieves 99.02% accuracy and an F1 of 0.994 on a held-out set of 50,000 real-world pairs, outperforming GPT-4o few-shot prompting and single-stage fine-tuning baselines.
  arXiv  Detail & Related papers  (2026-05-22T13:06:03Z)
• CorpusQA: A 10 Million Token Benchmark for Corpus-Level Analysis and Reasoning [48.56088080889236]
  We introduce CorpusQA, a new benchmark scaling up to 10 million tokens, generated via a novel data synthesis framework.<n>We show that finetuning on our synthesized data effectively enhances an LLM's general long-context reasoning capabilities.<n>Our findings indicate that memory-augmented agentic architectures offer a more robust alternative.
  arXiv  Detail & Related papers  (2026-01-21T12:52:30Z)
• Mitrasamgraha: A Comprehensive Classical Sanskrit Machine Translation Dataset [26.899919193282944]
  Mitrasamgraha is a high-quality Sanskrit-to-English machine translation dataset consisting of 391,548 bitext pairs.<n>It covers a time period of more than three millennia and a broad range of historical Sanskrit domains.<n>We conduct experiments benchmarking commercial and open models on this dataset and fine-tune NLLB and Gemma models on the dataset.
  arXiv  Detail & Related papers  (2026-01-12T08:37:15Z)
• LittiChoQA: Literary Texts in Indic Languages Chosen for Question Answering [33.849676909667]
  We introduce LittiChoQA, the largest literary QA dataset to date covering many languages spoken in the Gangetic plains of India.<n>The dataset comprises over 270K automatically generated question-answer pairs with a balanced distribution of factoid and non-factoid questions.
  arXiv  Detail & Related papers  (2026-01-06T13:59:41Z)
• Do LLMs Judge Distantly Supervised Named Entity Labels Well? Constructing the JudgeWEL Dataset [8.437906092903582]
  We present judgeWEL, a dataset for named entity recognition (NER) in Luxembourgish, automatically labelled and verified using large language models (LLM)<n>By exploiting internal links within Wikipedia articles, we infer entity types based on their corresponding Wikidata entries.<n>Because such links are not uniformly reliable, we mitigate noise by employing and comparing several LLMs to identify and retain only high-quality labelled sentences.
  arXiv  Detail & Related papers  (2026-01-01T17:53:38Z)
• ReMeREC: Relation-aware and Multi-entity Referring Expression Comprehension [29.50623143244436]
  ReMeREC aims to localize specified entities or regions in an image based on natural language descriptions.<n>We first construct a relation-aware, multi-entity REC dataset called ReMeX.<n>We then propose ReMeREC, a novel framework that jointly leverages visual and textual cues to localize multiple entities.
  arXiv  Detail & Related papers  (2025-07-22T11:23:48Z)
• PICASO: Permutation-Invariant Context Composition with State Space Models [98.91198288025117]
  State Space Models (SSMs) offer a promising solution by allowing a database of contexts to be mapped onto fixed-dimensional states.<n>We propose a simple mathematical relation derived from SSM dynamics to compose multiple states into one that efficiently approximates the effect of concatenating raw context tokens.<n>We evaluate our resulting method on WikiText and MSMARCO in both zero-shot and fine-tuned settings, and show that we can match the strongest performing baseline while enjoying on average 5.4x speedup.
  arXiv  Detail & Related papers  (2025-02-24T19:48:00Z)
• Context-Aware Hierarchical Merging for Long Document Summarization [56.96619074316232]
  We propose different approaches to enrich hierarchical merging with context from the source document.<n> Experimental results on datasets representing legal and narrative domains show that contextual augmentation consistently outperforms zero-shot and hierarchical merging baselines.
  arXiv  Detail & Related papers  (2025-02-03T01:14:31Z)
• Optimizing Language Model's Reasoning Abilities with Weak Supervision [48.60598455782159]
  We present textscPuzzleBen, a weakly supervised benchmark that comprises 25,147 complex questions, answers, and human-generated rationales. A unique aspect of our dataset is the inclusion of 10,000 unannotated questions, enabling us to explore utilizing fewer supersized data to boost LLMs' inference capabilities.
  arXiv  Detail & Related papers  (2024-05-07T07:39:15Z)
• Disambiguate Entity Matching using Large Language Models through Relation Discovery [1.6317061277457001]
  We propose a novel approach that shifts focus from purely semantic similarities to understanding and defining the "relations" between entities. By predefining a set of relations relevant to the task at hand, our method allows analysts to navigate the spectrum of similarity more effectively.
  arXiv  Detail & Related papers  (2024-03-26T03:07:32Z)
• SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languages [44.017657230247934]
  We present textitSemRel, a new semantic relatedness dataset collection annotated by native speakers across 13 languages. These languages originate from five distinct language families and are predominantly spoken in Africa and Asia. Each instance in the SemRel datasets is a sentence pair associated with a score that represents the degree of semantic textual relatedness between the two sentences.
  arXiv  Detail & Related papers  (2024-02-13T18:04:53Z)
• Entity Disambiguation with Entity Definitions [50.01142092276296]
  Local models have recently attained astounding performances in Entity Disambiguation (ED) Previous works limited their studies to using, as the textual representation of each candidate, only its Wikipedia title. In this paper, we address this limitation and investigate to what extent more expressive textual representations can mitigate it. We report a new state of the art on 2 out of 6 benchmarks we consider and strongly improve the generalization capability over unseen patterns.
  arXiv  Detail & Related papers  (2022-10-11T17:46:28Z)
• Knowledge-Rich Self-Supervised Entity Linking [58.838404666183656]
  Knowledge-RIch Self-Supervision ($tt KRISSBERT$) is a universal entity linker for four million UMLS entities. Our approach subsumes zero-shot and few-shot methods, and can easily incorporate entity descriptions and gold mention labels if available. Without using any labeled information, our method produces $tt KRISSBERT$, a universal entity linker for four million UMLS entities.
  arXiv  Detail & Related papers  (2021-12-15T05:05:12Z)
• Autoregressive Entity Retrieval [55.38027440347138]
  Entities are at the center of how we represent and aggregate knowledge. The ability to retrieve such entities given a query is fundamental for knowledge-intensive tasks such as entity linking and open-domain question answering. We propose GENRE, the first system that retrieves entities by generating their unique names, left to right, token-by-token in an autoregressive fashion.
  arXiv  Detail & Related papers  (2020-10-02T10:13:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.