Generating Event Causality Hypotheses through Semantic Relations

Author:

Hashimoto Chikara,Torisawa Kentaro,Kloetzer Julien,Oh Jong-Hoon

Abstract

Event causality knowledge is indispensable for intelligent natural language understanding. The problem is that any method for extracting event causalities from text is insufficient; it is likely that some event causalities that we can recognize in this world are not written in a corpus, no matter its size. We propose a method of hypothesizing unseen event causalities from known event causalities extracted from the web by the semantic relations between nouns. For example, our method can hypothesize "deploy a security camera" -> "avoid crimes" from "deploy a mosquito net" -> "avoid malaria" through semantic relation . Our experiments show that, from 2.4 million event causalities extracted from the web, our method generated more than 300,000 hypotheses, which were not in the input, with 70% precision. We also show that our method outperforms a state-of-the-art hypothesis generation method.

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Subject

General Medicine

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Event causality extraction through external event knowledge learning and polyhedral word embedding;Machine Learning;2024-01-22

2. Cross-lingual Related Events Recognition Methods Based on The Event Central News Sets;2023 38th Youth Academic Annual Conference of Chinese Association of Automation (YAC);2023-08-27

3. Construction and analysis of knowledge graphs for multi‐source heterogeneous data of soil pollution;Soil Use and Management;2023-04-04

4. Financial Risk Prediction Method Based on Texting Mining;2022 International Conference on Algorithms, Data Mining, and Information Technology (ADMIT);2022-09

5. Causality in requirements artifacts: prevalence, detection, and impact;Requirements Engineering;2022-02-09

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