# Learning to Predict from Textual Data

arXiv: https://arxiv.org/abs/1402.0574 · HTML: https://mlsanity.com/paper/1402.0574/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2014.

## Built on by (most cited first, 3 of 3)

- [Combining Machine Learning and Ontology: A Systematic Literature Review](https://mlsanity.com/paper/2401.07744/) (2024)
- [FarFetched: Entity-centric Reasoning and Claim Validation for the Greek Language based on Textually Represented Environments](https://mlsanity.com/paper/2407.09888/) (2024)
- [TAG-EQA: Text-And-Graph for Event Question Answering via Structured Prompting Strategies](https://mlsanity.com/paper/2510.01391/) (2025)

## Abstract (arXiv)

Given a current news event, we tackle the problem of generating plausible predictions of future events it might cause. We present a new methodology for modeling and predicting such future news events using machine learning and data mining techniques. Our Pundit algorithm generalizes examples of causality pairs to infer a causality predictor. To obtain precisely labeled causality examples, we mine 150 years of news articles and apply semantic natural language modeling techniques to headlines containing certain predefined causality patterns. For generalization, the model uses a vast number of world knowledge ontologies. Empirical evaluation on real news articles shows that our Pundit algorithm performs as well as non-expert humans.