| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_64c08dbbd66b52c9a43aa3682ae4aafe |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G10L2015-0633 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H10-60
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F16-335
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F40-289
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F16-3329
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G10L15-26
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G10L15-063
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F16-338
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F16-3344 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H10-60
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G10L15-06
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F40-289
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F16-338
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F16-335
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F16-33
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G10L15-26
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F16-332 |
| filingDate |
2022-04-15-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a7ab4195e6c5dd43a879675337bc8930
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_5774d9e025ed4d86b60bad3d6ebea78e
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a30c3d111f25caf7e5ae455a83843ec9
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4338c8cf062da2b2cbfb546b5fdd09bd
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_935be125244f262a508f3337d1ffc5de |
| publicationDate |
2022-06-28-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
CN-114678095-A |
| titleOfInvention |
A medical corpus expansion method based on deep language model and template customization |
| abstract |
The invention discloses a medical corpus expansion method based on a deep language model and template customization. Aiming at the problem that there are more professional terms of diseases, medicines and instruments in the corpus in the medical field than other scenarios, the invention adopts the template customization method to improve the medical The probability of professional nouns appearing in the generated corpus. For different types of diseases and between different departments, there are large differences in terms and terms, and it is unrealistic to manually produce large-scale templates for different diseases and different departments. A large number of diverse medical template sentences are generated centrally and automatically, thereby effectively expanding the medical corpus and reducing the time and economic cost of manual data collection. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115544213-A |
| priorityDate |
2022-04-15-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |