Despite the importance of understanding causality, corpora addressing causal relations are
limited. There is a discrepancy between existing annotation guidelines of event causality …

Researchers in natural language processing are paying more attention to causality mining.
Numerous applications of the growing need for efficient and accurate causality mining …

Causal knowledge is seen as one of the key ingredients to advance artificial intelligence.
Yet, few knowledge bases comprise causal knowledge to date, possibly due to significant …

The era of big textual corpora and machine learning technologies have paved the way for
researchers in numerous data mining fields. Among them, causality mining (CM) from textual …

Causal inference is fundamental to empirical scientific discoveries in natural and social
sciences; however, in the process of conducting causal inference, data management …

This study explores the potential of natural language models, including large language
models, to extract causal relations from medical texts, specifically from clinical practice …

Causality understanding between events is a critical natural language processing task that
is helpful in many areas, including health care, business risk management, and finance. On …

At least 5% of questions submitted to search engines ask about cause-effect relationships in
some way. To support the development of tailored approaches that can answer such …

Background The vaccination uptake rates of the human papillomavirus (HPV) vaccine
remain low despite the fact that the effectiveness of HPV vaccines has been established for …

Abstract Community Question Answering (CQA) has become an indispensable way for
modern people to share and acquire knowledge. It allows users to ask questions, which will …