# US 2022/0036007 A1 · Bootstrapping relation training data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0036007 A1 - Document ID: US20220036007A1 - Status: Published application - Kind: A1 - Application number: 16/944,396 - Filed: 2020-07-31 - Published: 2022-02-03 - Earliest claimed: 2020-07-31 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Pai-Fang Hsiao; Scott Carrier - Web page: https://patentgrep.com/patent/US20220036007A1 ## CPC - G06F 40/295: Named entity recognition - G06F 40/169: Annotation, e.g. comment data or footnotes - G06F 40/247: Thesauruses; Synonyms - G06F 40/284: Lexical analysis, e.g. tokenisation or collocates - G06F 40/30: Semantic analysis - G06N 3/0895: Weakly supervised learning, e.g. semi-supervised or self-supervised learning - G06N 3/09: Supervised learning - G16H 10/60: for patient-specific data, e.g. for electronic patient records - G16H 40/67: for remote operation - G06F 40/205: Parsing - G06N 3/08: Learning methods - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract Aspects of the invention include a computer-implemented method for bootstrapping relation training data. The method includes traversing a corpus to detect a first passage having a first set of co-occurring entities and intervening tokens associated with a relation type. Identifying a first predicate frame of the first passage based on the co-occurring entities and intervening tokens. Traversing the corpus again to detect a second passage having a second predicate frame with a same semantic structure as the first predicate frame, wherein the passage contains a second set of co-occurring entities associated with the relation during first instance that the processor did not detect during the first time. Detecting a second set of co-occurring entities in the second passage based on the second predicate frame. Annotating the second set of co-occurring entities to have a same relation as the first set of co-occurring entities. ## Claims ### Claim 1 A computer-implemented method comprising: traversing, by a processor for a first instance, a corpus to detect a first passage having a first set of co-occurring entities and intervening tokens associated with a desired relation type; identifying, by the processor, a first predicate frame of the first passage based on the co-occurring entities and intervening tokens; traversing, by the processor for a second instance, the corpus to detect a second passage having a second predicate frame with a same semantic structure as the first predicate frame, wherein the passage contains a second set of co-occurring entities associated with the desired relation type during first instance that the processor did not detect during the first instance; detecting, by the processor, a second set of co-occurring entities in the second passage based on the second predicate frame; and annotating, by the processor, the second set of co-occurring entities to have a same desired relation type as the first set of co-occurring entities. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein traversing the corpus to detect a second passage comprises: extracting keywords from the first predicate frame; converting each keyword into a keyword vector; and detecting additional keywords based on the respective word vectors, wherein each additional keyword can be represented by a respective word vector having a value within a threshold value of the keyword vectors. ### Claim 3 (depends on 2) The computer-implemented method of claim 2, wherein traversing the corpus to detect a second passage comprises: indexing each document in the corpus via an index keyword, wherein each index keyword is mapped to a document; searching the index keywords for matching additional keywords; retrieving a document from the corpus based on detecting an additional keyword. ### Claim 4 (depends on 2) The computer-implemented method of claim 2 further comprising: selecting a domain-specific thesaurus based on the desired relation type between co-occurring entities; and detecting the additional keywords from the domain specific thesaurus. ### Claim 5 (depends on 1) The computer-implemented method of claim 1 further comprising annotating the each entity of the second set of co-occurring entities to have a same entity type as a respective entity of the first set of co-occurring entities. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein the corpus is a medical based corpus. ### Claim 7 (depends on 1) The computer-implemented method of claim 1 further comprising: training a relation model to detect co-occurring entities having a desired relation type using the second passage. ### Claim 8 A system comprising: a memory having computer readable instructions; and one or more processors for executing the computer readable instructions, the computer readable instructions controlling the one or more processors to perform operations comprising: traversing, by a processor for a first instance, a corpus to detect a first passage having a first set of co-occurring entities and intervening tokens associated with a desired relation type; identifying, by the processor, a first predicate frame of the first passage based on the co-occurring entities and intervening tokens; traversing, by the processor for a second instance, the corpus to detect a second passage having a second predicate frame with a same semantic structure as the first predicate frame, wherein the passage contains a second set of co-occurring entities associated with the desired relation type during first instance that the processor did not detect during the first instance; detecting, by the processor, a second set of co-occurring entities in the second passage based on the second predicate frame; and annotating, by the processor, the second set of co-occurring entities to have a same desired relation type as the first set of co-occurring entities. ### Claim 9 (depends on 8) The system of claim 8, wherein traversing the corpus to detect a second passage comprises: extracting keywords from the first predicate frame; converting each keyword into a word vector; and detecting additional keywords based on the respective word vectors, wherein each additional keyword can be represented by a respective word vector having a value within a threshold value of the keyword vectors. ### Claim 10 (depends on 9) The system of claim 9, wherein traversing the corpus to detect a second passage comprises: indexing each document in the corpus via a keyword, wherein each keyword is mapped to a document; searching the index for additional keywords; retrieving a document from the corpus based on detecting an additional keyword. ### Claim 11 (depends on 9) The system of claim 9, wherein the operations further comprise: selecting a domain-specific thesaurus based on the desired relation type between co-occurring entities; and detecting the additional keywords from the domain specific thesaurus. ### Claim 12 (depends on 8) The system of claim 8, wherein the operations further comprise annotating the each entity of the second set of co-occurring entities to have a same entity type as a respective entity of the first set of co-occurring entities. ### Claim 13 (depends on 8) The system of claim 8, wherein the corpus is a medical based corpus. ### Claim 14 (depends on 8) The system of claim 8, wherein the operations further comprise: training a relation model to detect co-occurring entities having a desired relation type using the second passage. ### Claim 15 A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations comprising: traversing, by a processor for a first instance, a corpus to detect a first passage having a first set of co-occurring entities and intervening tokens associated with a desired relation type; identifying, by the processor, a first predicate frame of the first passage based on the co-occurring entities and intervening tokens; traversing, by the processor for a second instance, the corpus to detect a second passage having a second predicate frame with a same semantic structure as the first predicate frame, wherein the passage contains a second set of co-occurring entities associated with the desired relation type during first instance that the processor did not detect during the first instance; detecting, by the processor, a second set of co-occurring entities in the second passage based on the second predicate frame; and annotating, by the processor, the second set of co-occurring entities to have a same desired relation type as the first set of co-occurring entities. ### Claim 16 (depends on 15) The computer program product of claim 15, wherein traversing the corpus to detect a second passage comprises: extracting keywords from the first predicate frame; converting each keyword into a word vector; and detecting additional keywords based on the respective word vectors, wherein each additional keyword can be represented by a respective word vector having a value within a threshold value of the keyword vectors. ### Claim 17 (depends on 16) The computer program product of claim 16, wherein traversing the corpus to detect a second passage comprises: indexing each document in the corpus via a keyword, wherein each keyword is mapped to a document; searching the index for additional keywords; retrieving a document from the corpus based on detecting an additional keyword. ### Claim 18 (depends on 16) The computer program product of claim 16, wherein the operations further comprise: selecting a domain-specific thesaurus based on the desired relation type between co-occurring entities; and detecting the additional keywords from the domain specific thesaurus. ### Claim 19 (depends on 15) The computer program product of claim 15, wherein the operations further comprise annotating the each entity of the second set of co-occurring entities to have a same entity type as a respective entity of the first set of co-occurring entities. ### Claim 20 (depends on 15) The computer program product of claim 15, wherein the corpus is a medical based corpus.