| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_dd554226db6acd26c2c6b6051e3b54d6 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N5-046
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F40-268
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F40-284
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F40-289
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F40-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F40-205
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F40-211 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F40-289
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N5-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F40-284
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F40-205 |
| filingDate |
2021-03-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_5caa7accb4ea5ff6ea284087a4ea8b0b
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_030e1ce051c6793d210842f03269e24a
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_91ec4774eb26e078b8368940cd96be63
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_652db5e5a47f659c8144a7b841e29676
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a0623d1ab0fb6aeda6ad28e0abc82855
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_11295275f3c9e82f8bbe9bee269f6c04 |
| publicationDate |
2022-06-30-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2022207400-A1 |
| titleOfInvention |
Method and system for extraction of cause-effect relation from domain specific text |
| abstract |
This disclosure relates generally to extraction of cause-effect relation from domain specific text. Cause-effect relation highlights causal relationship among various entities, concepts and processes in a domain specific text. Conventional state-of-the-art methods use named entity recognition for extraction of cause-effect (CE) relation which does not give precise results. Embodiments of the present disclosure provide a knowledge-based approach for automatic extraction of CE relations from domain specific text. The present disclosure method is a combination of an unsupervised machine learning technique to discover causal triggers and a set of high-precision linguistic rules to identify cause/effect arguments of these causal triggers. The method extracts the CE relation in the form of a triplet comprising a causal trigger, a cause phrase and an effect phrase identified from the domain specific text. The disclosed method is used for extracting CE relations in biomedical text. |
| priorityDate |
2020-11-21-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |