Inproceedings,

Mining Health-related Cause-Effect Statements with High Precision at Large Scale

, , , , and .
Proceedings of the 29th International Conference on Computational Linguistics, page 1925--1936. Gyeongju, Republic of Korea, International Committee on Computational Linguistics, (October 2022)

Abstract

An efficient assessment of the health relatedness of text passages is important to mine the web at scale to conduct health sociological analyses or to develop a health search engine. We propose a new efficient and effective termhood score for predicting the health relatedness of phrases and sentences, which achieves 69\% recall at over 90\% precision on a web dataset with cause-effect statements. It is more effective than state-of-the-art medical entity linkers and as effective but much faster than BERT-based approaches. Using our method, we compile the Webis Medical CauseNet 2022, a new resource of 7.8 million health-related cause-effect statements such as ``Studies show that stress induces insomnia'' in which the cause (`stress') and effect (`insomnia') are labeled.

Tags

    Users

    • @scadsfct

    Comments and Reviews