Causal Pathway Extraction from Web-Board Documents
This research aim is to extract causal pathways, particularly disease causal pathways, through cause-effect relation (CErel) extraction from web-board documents. The causal pathways benefit people with a comprehensible representation approach to disease complication. A causative/effect-concept expre...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/21/10342 |
| _version_ | 1797512796083060736 |
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| author | Chaveevan Pechsiri Rapepun Piriyakul |
| author_facet | Chaveevan Pechsiri Rapepun Piriyakul |
| author_sort | Chaveevan Pechsiri |
| collection | DOAJ |
| description | This research aim is to extract causal pathways, particularly disease causal pathways, through cause-effect relation (CErel) extraction from web-board documents. The causal pathways benefit people with a comprehensible representation approach to disease complication. A causative/effect-concept expression is based on a verb phrase of an elementary discourse unit (EDU) or a simple sentence. The research has three main problems; how to determine CErel on an EDU-concept pair containing both causative and effect concepts in one EDU, how to extract causal pathways from EDU-concept pairs having CErel and how to indicate and represent implicit effect/causative-concept EDUs as implicit mediators with comprehension on extracted causal pathways. Therefore, we apply EDU’s word co-occurrence concept (wrdCoc) as an EDU-concept and the self-Cartesian product of a wrdCoc set from the documents for extracting wrdCoc pairs having CErel into a wrdCoc-pair set from the documents after learning CErel on wrdCoc pairs by supervised-machine learning. The wrdCoc-pair set is used for extracting the causal pathways by wrdCoc-pair matching through the documents. We then propose transitive closure and a dynamic template to indicate and represent the implicit mediators with the explicit ones. In contrast to previous works, the proposed approach enables causal-pathway extraction with high accuracy from the documents. |
| first_indexed | 2024-03-10T06:06:44Z |
| format | Article |
| id | doaj.art-9d85ae9a915a41d680dbfdf974c53dcd |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T06:06:44Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9d85ae9a915a41d680dbfdf974c53dcd2023-11-22T20:31:21ZengMDPI AGApplied Sciences2076-34172021-11-0111211034210.3390/app112110342Causal Pathway Extraction from Web-Board DocumentsChaveevan Pechsiri0Rapepun Piriyakul1College of Innovative Technology and Engineering, Dhurakij Pundit University, Bangkok 10210, ThailandDepartment of Computer Science, Ramkhamhaeng University, Bangkok 10240, ThailandThis research aim is to extract causal pathways, particularly disease causal pathways, through cause-effect relation (CErel) extraction from web-board documents. The causal pathways benefit people with a comprehensible representation approach to disease complication. A causative/effect-concept expression is based on a verb phrase of an elementary discourse unit (EDU) or a simple sentence. The research has three main problems; how to determine CErel on an EDU-concept pair containing both causative and effect concepts in one EDU, how to extract causal pathways from EDU-concept pairs having CErel and how to indicate and represent implicit effect/causative-concept EDUs as implicit mediators with comprehension on extracted causal pathways. Therefore, we apply EDU’s word co-occurrence concept (wrdCoc) as an EDU-concept and the self-Cartesian product of a wrdCoc set from the documents for extracting wrdCoc pairs having CErel into a wrdCoc-pair set from the documents after learning CErel on wrdCoc pairs by supervised-machine learning. The wrdCoc-pair set is used for extracting the causal pathways by wrdCoc-pair matching through the documents. We then propose transitive closure and a dynamic template to indicate and represent the implicit mediators with the explicit ones. In contrast to previous works, the proposed approach enables causal-pathway extraction with high accuracy from the documents.https://www.mdpi.com/2076-3417/11/21/10342cause-effect relationtransitive closureword co-occurrence |
| spellingShingle | Chaveevan Pechsiri Rapepun Piriyakul Causal Pathway Extraction from Web-Board Documents Applied Sciences cause-effect relation transitive closure word co-occurrence |
| title | Causal Pathway Extraction from Web-Board Documents |
| title_full | Causal Pathway Extraction from Web-Board Documents |
| title_fullStr | Causal Pathway Extraction from Web-Board Documents |
| title_full_unstemmed | Causal Pathway Extraction from Web-Board Documents |
| title_short | Causal Pathway Extraction from Web-Board Documents |
| title_sort | causal pathway extraction from web board documents |
| topic | cause-effect relation transitive closure word co-occurrence |
| url | https://www.mdpi.com/2076-3417/11/21/10342 |
| work_keys_str_mv | AT chaveevanpechsiri causalpathwayextractionfromwebboarddocuments AT rapepunpiriyakul causalpathwayextractionfromwebboarddocuments |