# Analyzing sports commentary in order to automatically recognize events and extract insights

arXiv: https://arxiv.org/abs/2307.10303 · HTML: https://mlsanity.com/paper/2307.10303/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 18 Jul 2023.

## Builds on (most cited first, 3 of 3)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [State-of-the-art Speech Recognition With Sequence-to-Sequence Models](https://mlsanity.com/paper/1712.01769/) (2017)

## Built on by (most cited first, 2 of 2)

- [Posture-Driven Action Intent Inference for Playing style and Fatigue Assessment](https://mlsanity.com/paper/2507.11642/) (2025)
- [SUMMIR: A Hallucination-Aware Framework for Ranking Sports Insights from LLMs](https://mlsanity.com/paper/2604.04947/) (2026)

## Abstract (arXiv)

In this paper, we carefully investigate how we can use multiple different Natural Language Processing techniques and methods in order to automatically recognize the main actions in sports events. We aim to extract insights by analyzing live sport commentaries from different sources and by classifying these major actions into different categories. We also study if sentiment analysis could help detect these main actions.