{"id":"https://openalex.org/W4408403654","doi":"https://doi.org/10.1145/3677389.3702579","title":"MPR: A Dataset for Extracting Relations Between Method Entities and Scientific Papers","display_name":"MPR: A Dataset for Extracting Relations Between Method Entities and Scientific Papers","publication_year":2024,"publication_date":"2024-12-16","ids":{"openalex":"https://openalex.org/W4408403654","doi":"https://doi.org/10.1145/3677389.3702579"},"language":"en","primary_location":{"id":"doi:10.1145/3677389.3702579","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3677389.3702579","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM/IEEE Joint Conference on Digital Libraries","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/A5112431557","display_name":"Yongxin He","orcid":null},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yongxin He","raw_affiliation_strings":["School of Government, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Government, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002621971","display_name":"Gary Gu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210144143","display_name":"Inspur (China)","ror":"https://ror.org/0474p4r72","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210144143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Gu","raw_affiliation_strings":["Inspur Software Technology Co.,Ltd, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Inspur Software Technology Co.,Ltd, Beijing, China","institution_ids":["https://openalex.org/I4210144143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113913281","display_name":"Chong Chen","orcid":"https://orcid.org/0000-0002-9704-1575"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chong Chen","raw_affiliation_strings":["Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111726041","display_name":"Hongfei Yan","orcid":"https://orcid.org/0000-0001-5914-8585"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongfei Yan","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5112431557"],"corresponding_institution_ids":["https://openalex.org/I25254941"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.254605,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9950000047683716,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9950000047683716,"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":0.9941999912261963,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9926999807357788,"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/computer-science","display_name":"Computer science","score":0.7080937027931213},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5351819396018982},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3864366412162781},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32334715127944946}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7080937027931213},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5351819396018982},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3864366412162781},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32334715127944946}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3677389.3702579","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3677389.3702579","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM/IEEE Joint Conference on Digital Libraries","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G707542906","display_name":null,"funder_award_id":"21BTQ065","funder_id":"https://openalex.org/F4320335869","funder_display_name":"National Social Science Fund of China"}],"funders":[{"id":"https://openalex.org/F4320335869","display_name":"National Social Science Fund of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1622429627","https://openalex.org/W2037524964","https://openalex.org/W2250803090","https://openalex.org/W2799125718","https://openalex.org/W2803370961","https://openalex.org/W2806882588","https://openalex.org/W2899489554","https://openalex.org/W2941003925","https://openalex.org/W2962903510","https://openalex.org/W2963718112","https://openalex.org/W2970771982","https://openalex.org/W3035372073","https://openalex.org/W3046339272","https://openalex.org/W3092095014","https://openalex.org/W3095863931","https://openalex.org/W3104333731","https://openalex.org/W3120655260","https://openalex.org/W3160963856","https://openalex.org/W4220942703","https://openalex.org/W4308972505","https://openalex.org/W4382201621","https://openalex.org/W4384028630","https://openalex.org/W4391365094","https://openalex.org/W4399457603","https://openalex.org/W6765286763"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"The":[0,138,193],"method":[1,17,29,52,55,81,86,102,143],"entities":[2,18,103,144],"are":[3,160,195],"the":[4,7,13,25,51,77,94,115,130,134,149,164,167,181,185,215,221],"distillation":[5],"of":[6,15,27,47,79,96,129,184,217],"knowledge":[8,56,212],"within":[9,214],"scientific":[10,218],"papers.":[11],"However,":[12],"contribution":[14],"different":[16],"to":[19,71,141,162,197],"a":[20,28,31,35,48,72,97,127,177,201],"study":[21,113],"varies.":[22],"Therefore,":[23],"discerning":[24],"relation":[26,131],"and":[30,58,60,124,145,174,207],"paper":[32,49,151],"has":[33],"become":[34],"common":[36],"concern":[37],"in":[38,44,54,92,152,210,220],"many":[39],"application":[40],"scenarios":[41],"such":[42],"as":[43,117,200],"impact":[45],"evaluation":[46],"from":[50],"dimension,":[53],"acquisition":[57],"retrieval":[59],"so":[61],"on.":[62],"Nevertheless,":[63],"existing":[64],"studies":[65],"have":[66],"often":[67],"simplified":[68],"this":[69,110,112],"issue":[70],"text":[73],"classification":[74],"task,":[75],"modeling":[76],"semantics":[78],"entire":[80],"sentences":[82],"rather":[83],"than":[84],"individual":[85],"entities.":[87],"This":[88],"approach":[89],"falls":[90],"short":[91],"addressing":[93],"complexity":[95],"single":[98],"sentence":[99],"containing":[100],"multiple":[101],"with":[104,148,166],"inconsistent":[105],"semantic":[106],"relations.":[107],"To":[108],"overcome":[109],"limitation,":[111],"formalizes":[114],"problem":[116],"an":[118,153],"end-to-end":[119],"named":[120],"entity":[121],"recognition":[122],"task":[123],"introduces":[125],"MPR,":[126],"dataset":[128,139,187],"labels":[132],"for":[133,180,203],"sequence":[135],"labeling":[136],"task.":[137,182],"supports":[140],"identify":[142],"their":[146],"relations":[147],"host":[150],"integrated":[154],"process.":[155],"Several":[156],"deep":[157],"learning-based":[158],"models":[159],"developed":[161],"solve":[163],"problem,":[165],"SciBERT-based":[168],"algorithm":[169],"showing":[170],"particularly":[171],"excellent":[172],"performance":[173,206],"thus":[175],"establishing":[176],"strong":[178],"baseline":[179],"Part":[183],"MPR":[186],"is":[188],"publicly":[189],"accessible":[190],"at":[191],"https://github.com/ChenCbnu/methodanno.":[192],"communities":[194],"welcome":[196],"utilize":[198],"it":[199],"foundation":[202],"exploring":[204],"advanced":[205],"promising":[208],"directions":[209],"fine-grained":[211],"mining":[213],"domain":[216],"literature":[219],"new":[222],"technological":[223],"era.":[224]},"counts_by_year":[],"updated_date":"2025-12-28T23:10:05.387466","created_date":"2025-10-10T00:00:00"}