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

arXiv: https://arxiv.org/abs/2306.09505 · HTML: https://mlsanity.com/paper/2306.09505/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 41 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 8 of 8)

- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [EventKG: A Multilingual Event-Centric Temporal Knowledge Graph](https://mlsanity.com/paper/1804.04526/) (2018)
- [Men Are Elected, Women Are Married: Events Gender Bias on Wikipedia](https://mlsanity.com/paper/2106.01601/) (2021)
- [Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Texts](https://mlsanity.com/paper/2008.02250/) (2020)
- [Discovering Differences in the Representation of People using Contextualized Semantic Axes](https://mlsanity.com/paper/2210.12170/) (2022)
- [The URW-KG: a Resource for Tackling the Underrepresentation of non-Western Writers](https://mlsanity.com/paper/2212.13104/) (2022)
- [BiographyNet: Extracting Relations Between People and Events](https://mlsanity.com/paper/1801.07073/) (2018)

## Built on by (most cited first, 8 of 8)

- [Unfamiliar Finetuning Examples Control How Language Models Hallucinate](https://mlsanity.com/paper/2403.05612/) (2024)
- [Evaluating and Enhancing Large Language Models for Novelty Assessment in Scholarly Publications](https://mlsanity.com/paper/2409.16605/) (2024)
- [SelfCheck-Eval: A Multi-Module Framework for Zero-Resource Hallucination Detection in Large Language Models](https://mlsanity.com/paper/2502.01812/) (2025)
- [What Are They Filtering Out? An Experimental Benchmark of Filtering Strategies for Harm Reduction in Pretraining Datasets](https://mlsanity.com/paper/2503.05721/) (2025)
- [The World Literature Knowledge Graph](https://mlsanity.com/paper/2307.16659/) (2023)
- [From Parameters to Prompts: Understanding and Mitigating the Factuality Gap between Fine-Tuned LLMs](https://mlsanity.com/paper/2505.23410/) (2025)
- [BHRAM-IL: A Benchmark for Hallucination Recognition and Assessment in Multiple Indian Languages](https://mlsanity.com/paper/2512.01852/) (2025)
- [Conformal Privacy Auditing: Calibrated Re-identification Attacks with Statistical Guarantees](https://mlsanity.com/paper/2609.21340/) (2026)

## 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.