Causal Extraction from the Literature of Pressure Injury and Risk Factors

Guo, Siyi; Jin, Liuqi; Yang, Jiaoyun; Jiang, Mengyao; Han, Lin; An, Ning

doi:10.1109/icbk50248.2020.00087

Cited by 3 publications

(1 citation statement)

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“…They propose the use of BERT [9], a transformer based language model, to extract causal relations from text, and store them in in the form of a Knowledge Graphs (KG), after being refereed by experts. Guo et al have also used an unsupervised learning model to extract causal relations between pressure injury and risk factors [33], to construct a causal graph. In [34], Veitch et al explored the possibility of using causality from text to understand what affects a scientific paper's acceptance.…”

Section: Related Workmentioning

confidence: 99%

A Joint Model for Detecting Causal Sentences and Cause-Effect Relations from Text

Dasgupta

Naskar

Dey

et al. 2022

Towards a Knowledge-Aware AI

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Text documents are rich repositories of causal knowledge. While journal publications typically contain analytical explanations of observations on the basis of scientific experiments conducted by researchers, analyst reports, News articles or even consumer generated text contain not only viewpoints of authors, but often contain causal explanations for those viewpoints. As interest in data science shifts towards understanding causality rather than mere correlations, there is also a surging interest in extracting causal constructs from text to provide augmented information for better decision making. Causality extraction from text is viewed as a relation extraction problem which requires identification of causal sentences as well as detection of cause and effect clauses separately. In this paper, we present a joint model for causal sentence classification and extraction of cause and effect clauses, using a sequence-labeling architecture cascaded with fine-tuned Bidirectional Encoder Representations from Transformers (BERT) language model. The cause and effect clauses are further processed to identify named entities and build a causal graph using domain constraints. We have done multiple experiments to assess the generalizability of the model. It is observed that when fine-tuned with sentences from a mixed corpus, and further trained to solve both the tasks correctly, the model learns the nuances of expressing causality independent of the domain. The proposed model has been evaluated against multiple state-of-the-art models proposed in literature and found to outperform them all.

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Section: Related Workmentioning

confidence: 99%