{"id":"https://openalex.org/W2185509808","doi":"https://doi.org/10.1109/icmlc.2015.7340672","title":"Bootstrapping-based relation extraction in financial domain","display_name":"Bootstrapping-based relation extraction in financial domain","publication_year":2015,"publication_date":"2015-07-01","ids":{"openalex":"https://openalex.org/W2185509808","doi":"https://doi.org/10.1109/icmlc.2015.7340672","mag":"2185509808"},"language":"en","primary_location":{"id":"doi:10.1109/icmlc.2015.7340672","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2015.7340672","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Conference on Machine Learning and Cybernetics (ICMLC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5104122533","display_name":"Bing Kong","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bing Kong","raw_affiliation_strings":["Shenzhen Engineering Laboratory of Performance Robots at Digital Stage, Shenzhen Graduate School, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Engineering Laboratory of Performance Robots at Digital Stage, Shenzhen Graduate School, Shenzhen, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018149714","display_name":"Ruifeng Xu","orcid":"https://orcid.org/0000-0001-9885-2364"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rui-Feng Xu","raw_affiliation_strings":["Shenzhen Engineering Laboratory of Performance Robots at Digital Stage, Shenzhen Graduate School, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Engineering Laboratory of Performance Robots at Digital Stage, Shenzhen Graduate School, Shenzhen, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007835437","display_name":"Dongyin Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong-Yin Wu","raw_affiliation_strings":["Shenzhen Engineering Laboratory of Performance Robots at Digital Stage, Shenzhen Graduate School, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Engineering Laboratory of Performance Robots at Digital Stage, Shenzhen Graduate School, Shenzhen, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5104122533"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8629,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.82638032,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"19","issue":null,"first_page":"897","last_page":"903"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/bootstrapping","display_name":"Bootstrapping (finance)","score":0.8347473740577698},{"id":"https://openalex.org/keywords/substring","display_name":"Substring","score":0.8337222337722778},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7636333703994751},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.7375496625900269},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.7228410243988037},{"id":"https://openalex.org/keywords/pattern-matching","display_name":"Pattern matching","score":0.5671992897987366},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5586615800857544},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5203111171722412},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.47661295533180237},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4541357159614563},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.43138113617897034},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3799425959587097},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36201801896095276},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1452796757221222},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.13841789960861206},{"id":"https://openalex.org/keywords/data-structure","display_name":"Data structure","score":0.1113932728767395},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08002263307571411},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.07067900896072388}],"concepts":[{"id":"https://openalex.org/C207609745","wikidata":"https://www.wikidata.org/wiki/Q4944086","display_name":"Bootstrapping (finance)","level":2,"score":0.8347473740577698},{"id":"https://openalex.org/C182407805","wikidata":"https://www.wikidata.org/wiki/Q2626534","display_name":"Substring","level":3,"score":0.8337222337722778},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7636333703994751},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.7375496625900269},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.7228410243988037},{"id":"https://openalex.org/C68859911","wikidata":"https://www.wikidata.org/wiki/Q1503724","display_name":"Pattern matching","level":2,"score":0.5671992897987366},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5586615800857544},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5203111171722412},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.47661295533180237},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4541357159614563},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.43138113617897034},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3799425959587097},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36201801896095276},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1452796757221222},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.13841789960861206},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.1113932728767395},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08002263307571411},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.07067900896072388},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmlc.2015.7340672","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2015.7340672","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Conference on Machine Learning and Cybernetics (ICMLC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1493490255","https://openalex.org/W2012179495","https://openalex.org/W2029915058","https://openalex.org/W2053238041","https://openalex.org/W2090125415","https://openalex.org/W2103931177","https://openalex.org/W2108211831","https://openalex.org/W2123143128","https://openalex.org/W2124008094","https://openalex.org/W2125553157","https://openalex.org/W2127978399","https://openalex.org/W2152269015","https://openalex.org/W2162590473","https://openalex.org/W2348630004","https://openalex.org/W2350326008","https://openalex.org/W2383767851","https://openalex.org/W4285719527","https://openalex.org/W6676183483","https://openalex.org/W6678778854","https://openalex.org/W6683883671"],"related_works":["https://openalex.org/W2902555313","https://openalex.org/W2731201547","https://openalex.org/W2135574162","https://openalex.org/W2890359459","https://openalex.org/W4249054680","https://openalex.org/W2055497872","https://openalex.org/W2902482624","https://openalex.org/W2943461603","https://openalex.org/W2218650848","https://openalex.org/W2091929432"],"abstract_inverted_index":{"Relation":[0],"extraction":[1,116],"plays":[2],"an":[3],"important":[4],"role":[5],"in":[6,101],"many":[7],"natural":[8],"language":[9],"processing":[10],"tasks,":[11],"such":[12],"as":[13],"knowledge":[14],"graph":[15],"and":[16,38,80],"question":[17],"answering":[18],"system.":[19],"This":[20],"paper":[21],"presents":[22],"a":[23],"novel":[24],"method":[25,117],"to":[26,53,76,97],"extract":[27],"relation":[28,35,115],"from":[29,57],"Chinese":[30,99],"financial":[31],"news":[32],"by":[33],"incorporating":[34],"pattern":[36,41,88],"matching":[37,54],"bootstrapping":[39],"based":[40],"expansion.":[42],"The":[43,60,87],"seed":[44],"patterns":[45,62,79,94],"are":[46,51,63,95],"firstly":[47],"manually":[48],"compiled.":[49],"They":[50],"applied":[52,96],"the":[55,68,74,82,106],"sentences":[56,75],"unlabeled":[58],"text":[59,100],"new":[61],"then":[64],"discovered":[65],"through":[66],"finding":[67],"maximum":[69],"common":[70],"substring":[71],"sequences":[72],"between":[73],"generate":[77],"candidate":[78,85],"estimating":[81],"quality":[83],"of":[84],"patterns.":[86],"lib":[89],"is":[90],"expanded":[91],"iteratively.":[92],"These":[93],"running":[98],"finance":[102],"domain":[103],"for":[104],"extracting":[105],"target":[107],"relations.":[108],"Experimental":[109],"results":[110],"show":[111],"that":[112],"our":[113],"proposed":[114],"achieves":[118],"good":[119],"performance":[120],"with":[121],"few":[122],"labeled":[123],"data.":[124]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}