http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115017909-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_d40285e27f4ed83b8728b0d477404958 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B50-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F40-30 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F40-211 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F40-295 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B50-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F40-295 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F40-211 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F40-30 |
| filingDate | 2022-06-09-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_eb8f5d66d3db27ead13bc0c98ead7479 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_57ed6248586b7f4f17bd0b49c1deb0c2 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_f667557cd2c931ed86f7718c2aa9ec7e http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a2d412d0669df523ebe6e92a7f4c156d |
| publicationDate | 2022-09-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-115017909-A |
| titleOfInvention | A biological nested named entity recognition method based on attention state transfer model |
| abstract | A biologically nested named entity recognition method based on the attention state transfer model, including: 1. Divide the biological domain text containing five types of entity labels of DNA, RNA, protein, cell line and cell into training data and test data ; 2. According to the input form of the attention state transfer model and the semantic mask model, adjust the training data to a form that satisfies the model input; 3. Train the attention state transfer model to learn the correlation between words, The candidate entity can be extracted from the text through the state output by the model and its type can be judged; 4. The semantic mask model is trained to judge whether the candidate entity and its type conform to the context semantics; 5. The test data is input to the attention state transfer In the model, candidate entities are extracted, and then the extracted entities are masked and sent to the semantic mask model for screening, and finally the real entities that fit the context are confirmed. |
| priorityDate | 2022-06-09-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID516892 http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID453034310 |
Total number of triples: 22.