CausalTriad

Abstract

Deriving pseudo causal relations from medical text data lies at the heart of medical literature mining. Existing studies have utilized extraction models to find pseudo causal relation from single sentences, while the knowledge created by causation transitivity - often spanning multiple sentences - has not been considered. Furthermore, we observe that many pseudo causal relations follow the rule of causation transitivity, which makes it possible to discover unseen casual relations and generate new causal relation hypotheses. In this paper, we address these two issues by proposing a factor graph model to incorporate three clues to discover causation expressions in the text data. We propose four types of triad structures to represent the rules of causation transitivity among causal relations. Our proposed model, called CausalTriad, uses textual and structural knowledge to infer pseudo causal relations from the triad structures. Experimental results on two datasets demonstrate that (a) CausalTriad is effective for pseudo causal relation discovery within and across sentences; (b) CausalTriad is highly capable at recognizing implicit pseudo causal relations; (c) CausalTriad can infer missing/new pseudo causal relations from text data.