- 专利标题: Causal Knowledge Identification and Extraction
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申请号: US17354171申请日: 2021-06-22
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公开(公告)号: US20220405487A1公开(公告)日: 2022-12-22
- 发明人: Manik Bhandari , Oktie Hassanzadeh , Mark David Feblowitz , Kavitha Srinivas , Shirin Sohrabi Araghi
- 申请人: International Business Machines Corporation
- 申请人地址: US NY Armonk
- 专利权人: International Business Machines Corporation
- 当前专利权人: International Business Machines Corporation
- 当前专利权人地址: US NY Armonk
- 主分类号: G06F40/40
- IPC分类号: G06F40/40 ; G06N20/00 ; G06N5/04
摘要:
A computer-implemented method is provided that includes accessing candidate text and a candidate pair including first and second phrases, substituting the first and second phrases into cause-effect patterns to generate variant sentences. An artificial intelligence model is leveraged to determine respective probabilities that the variant sentences are inferred from the candidate text, calculate a statistical measure of the respective probabilities, and assess the calculated statistical measure to ascertain whether the first and second phrases possess a causal relationship or non-causal relationship to one another. A knowledge base including one or more pairs of cause-effect phrase pairs is populated with the first and second phrases possessing the causal relationship. A computer system and a computer program product are also provided.
公开/授权文献
- US11922129B2 Causal knowledge identification and extraction 公开/授权日:2024-03-05
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