mlsanityData to 13 Oct 2026

Wikibio: a Semantic Resource for the Intersectional Analysis of Biographical Events

arXiv 2306.09505 ↗First posted 15 Jun 2023cs.CLcited by 8

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Abstract (arXiv)

Biographical event detection is a relevant task for the exploration and comparison of the ways in which people's lives are told and represented. In this sense, it may support several applications in digital humanities and in works aimed at exploring bias about minoritized groups. Despite that, there are no corpora and models specifically designed for this task. In this paper we fill this gap by presenting a new corpus annotated for biographical event detection. The corpus, which includes 20 Wikipedia biographies, was compared with five existing corpora to train a model for the biographical event detection task. The model was able to detect all mentions of the target-entity in a biography with an F-score of 0.808 and the entity-related events with an F-score of 0.859. Finally, the model was used for performing an analysis of biases about women and non-Western people in Wikipedia biographies.

Key facts. Cited by 8 papers in the map (data through 13 Oct 2026). Builds on 8 of its 41 references that are in the map. First posted on arXiv 15 Jun 2023.

Builds on

8 of 41 references.

References · 8

  1. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,281
  2. 2019SpanBERT: Improving Pre-training by Representing and Predicting Spanscited by 833
  3. 2018EventKG: A Multilingual Event-Centric Temporal Knowledge Graphcited by 43
  4. 2021Men Are Elected, Women Are Married: Events Gender Bias on Wikipediacited by 30
  5. 2020Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Textscited by 21
  6. 2022Discovering Differences in the Representation of People using Contextualized Semantic Axescited by 12
  7. 2022The URW-KG: a Resource for Tackling the Underrepresentation of non-Western Writerscited by 2
  8. 2018BiographyNet: Extracting Relations Between People and Eventscited by 1

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 4; 2026: 1.

8 papers.

  1. 2024Unfamiliar Finetuning Examples Control How Language Models Hallucinatecited by 86
  2. 2024Evaluating and Enhancing Large Language Models for Novelty Assessment in Scholarly Publicationscited by 26
  3. 2025SelfCheck-Eval: A Multi-Module Framework for Zero-Resource Hallucination Detection in Large Language Modelscited by 4
  4. 2023The World Literature Knowledge Graphcited by 1
  5. 2025What Are They Filtering Out? An Experimental Benchmark of Filtering Strategies for Harm Reduction in Pretraining Datasetscited by 1
  6. 2025From Parameters to Prompts: Understanding and Mitigating the Factuality Gap between Fine-Tuned LLMscited by 1
  7. 2025BHRAM-IL: A Benchmark for Hallucination Recognition and Assessment in Multiple Indian Languagescited by 1
  8. 2026Conformal Privacy Auditing: Calibrated Re-identification Attacks with Statistical Guaranteescited by 0