prateekguptaiiitk/Causal_Relation_Extraction
Causal Relation Extraction and Identification using Conditional Random Fields
This project helps medical researchers, epidemiologists, or clinical analysts automatically identify cause-and-effect relationships within medical texts. It takes unstructured medical documents as input and extracts explicit causal statements, such as "Hunger is the most common cause of crying in a young baby," highlighting the cause, effect, and the linking relation. The output is a structured identification of these causal pairs, streamlining the analysis of medical literature.
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Use this if you need to quickly pinpoint and extract explicit cause-and-effect relationships from a large volume of medical documents without manual reading.
Not ideal if you need to detect implicit causal relationships, or if your text is not from the medical domain, as this model is specifically tuned for medical language.
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Jupyter Notebook
License
MIT
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Last pushed
Jul 27, 2019
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