{"id":"https://openalex.org/W3114203687","doi":"https://doi.org/10.18653/v1/2020.coling-main.507","title":"A Neural Model for Aggregating Coreference Annotation in Crowdsourcing","display_name":"A Neural Model for Aggregating Coreference Annotation in Crowdsourcing","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3114203687","doi":"https://doi.org/10.18653/v1/2020.coling-main.507","mag":"3114203687"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2020.coling-main.507","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2020.coling-main.507","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th International Conference on Computational Linguistics","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.18653/v1/2020.coling-main.507","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100724549","display_name":"Maolin Li","orcid":"https://orcid.org/0000-0002-0828-2001"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Maolin Li","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042127858","display_name":"Hiroya Takamura","orcid":"https://orcid.org/0000-0002-3244-8294"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hiroya Takamura","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5077976343","display_name":"Sophia Ananiadou","orcid":"https://orcid.org/0000-0002-4097-9191"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sophia Ananiadou","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100724549"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.965,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.88954796,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"5760","last_page":"5773"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.9958000183105469,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9912999868392944,"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/coreference","display_name":"Coreference","score":0.9518824219703674},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8592125177383423},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.817306399345398},{"id":"https://openalex.org/keywords/crowdsourcing","display_name":"Crowdsourcing","score":0.7685528993606567},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6716903448104858},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6419795155525208},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6207394599914551},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.6158942580223083},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5732993483543396},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5544523000717163},{"id":"https://openalex.org/keywords/resolution","display_name":"Resolution (logic)","score":0.5265122652053833},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5181295275688171},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4677215814590454},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4586382806301117},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.36473095417022705},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3377440869808197}],"concepts":[{"id":"https://openalex.org/C28076734","wikidata":"https://www.wikidata.org/wiki/Q63087","display_name":"Coreference","level":3,"score":0.9518824219703674},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8592125177383423},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.817306399345398},{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.7685528993606567},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6716903448104858},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6419795155525208},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6207394599914551},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.6158942580223083},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5732993483543396},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5544523000717163},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.5265122652053833},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5181295275688171},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4677215814590454},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4586382806301117},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.36473095417022705},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3377440869808197},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2020.coling-main.507","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2020.coling-main.507","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th International Conference on Computational Linguistics","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2020.coling-main.507","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2020.coling-main.507","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th International Conference on Computational Linguistics","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4365314885","display_name":null,"funder_award_id":"MR/N00583X/1","funder_id":"https://openalex.org/F4320334626","funder_display_name":"Medical Research Council"},{"id":"https://openalex.org/G6457786194","display_name":null,"funder_award_id":"BB/P025684/1","funder_id":"https://openalex.org/F4320334629","funder_display_name":"Biotechnology and Biological Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334626","display_name":"Medical Research Council","ror":"https://ror.org/03x94j517"},{"id":"https://openalex.org/F4320334629","display_name":"Biotechnology and Biological Sciences Research Council","ror":"https://ror.org/00cwqg982"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W9014458","https://openalex.org/W576550507","https://openalex.org/W1821270946","https://openalex.org/W1880262756","https://openalex.org/W1965555277","https://openalex.org/W1965693266","https://openalex.org/W1970381522","https://openalex.org/W2000363133","https://openalex.org/W2067760738","https://openalex.org/W2080527295","https://openalex.org/W2098345921","https://openalex.org/W2101268022","https://openalex.org/W2112511942","https://openalex.org/W2129494713","https://openalex.org/W2134305421","https://openalex.org/W2135669016","https://openalex.org/W2155069789","https://openalex.org/W2187089797","https://openalex.org/W2250244475","https://openalex.org/W2250741536","https://openalex.org/W2250854390","https://openalex.org/W2251746595","https://openalex.org/W2251818274","https://openalex.org/W2290431464","https://openalex.org/W2295586832","https://openalex.org/W2407338347","https://openalex.org/W2574762171","https://openalex.org/W2575233155","https://openalex.org/W2585226541","https://openalex.org/W2739753637","https://openalex.org/W2740579382","https://openalex.org/W2740614682","https://openalex.org/W2745665559","https://openalex.org/W2759633878","https://openalex.org/W2786519160","https://openalex.org/W2891045077","https://openalex.org/W2912010834","https://openalex.org/W2920114910","https://openalex.org/W2943899420","https://openalex.org/W2949681443","https://openalex.org/W2962739339","https://openalex.org/W2963341956","https://openalex.org/W2963414797","https://openalex.org/W2963741336","https://openalex.org/W2963772355","https://openalex.org/W2964121744","https://openalex.org/W2964179635","https://openalex.org/W3031183459","https://openalex.org/W3092024499"],"related_works":["https://openalex.org/W2139373276","https://openalex.org/W2227889443","https://openalex.org/W2905433371","https://openalex.org/W3032998312","https://openalex.org/W135177976","https://openalex.org/W1509033667","https://openalex.org/W4385749782","https://openalex.org/W3215967424","https://openalex.org/W2396571892","https://openalex.org/W3120396479"],"abstract_inverted_index":{"Coreference":[0],"resolution":[1,31],"is":[2,33,62],"the":[3,15,43,66,71,79,94,112,118,131,145,149,155,162],"task":[4],"of":[5,46,97,134,157,175],"identifying":[6],"all":[7],"mentions":[8],"in":[9,148,160],"a":[10,28,172],"text":[11],"that":[12],"refer":[13],"to":[14,26,41,64,129],"same":[16,113],"real-world":[17],"entity.":[18],"Collecting":[19],"sufficient":[20],"labelled":[21],"data":[22,47],"from":[23],"expert":[24],"annotators":[25],"train":[27],"high-performance":[29],"coreference":[30,88,132],"system":[32],"time-consuming":[34],"and":[35,49,87,122],"expensive.":[36],"Crowdsourcing":[37],"makes":[38],"it":[39,61],"possible":[40],"obtain":[42],"required":[44],"amounts":[45],"rapidly":[48],"cost-effectively.":[50],"However,":[51],"crowd-sourced":[52],"labels":[53,68],"can":[54],"be":[55],"noisy.":[56],"To":[57],"ensure":[58],"high-quality":[59],"data,":[60],"crucial":[63],"infer":[65],"correct":[67,163],"by":[69],"aggregating":[70],"noisy":[72],"labels.":[73,164],"In":[74],"this":[75],"paper,":[76],"we":[77,92,137],"split":[78],"aggregation":[80],"into":[81,116,143],"two":[82],"subtasks,":[83],"i.e,":[84],"mention":[85,99],"classification":[86],"chain":[89,133],"inference.":[90],"Firstly,":[91],"predict":[93],"general":[95],"class":[96],"each":[98,108,135],"using":[100],"an":[101],"autoencoder,":[102],"which":[103,141],"incorporates":[104],"contextual":[105],"information":[106],"about":[107],"mention,":[109,136],"while":[110],"at":[111,125],"time":[114],"taking":[115],"account":[117,144],"mention\u2019s":[119],"annotation":[120],"complexity":[121],"annotators\u2019":[123],"reliability":[124,147],"different":[126],"levels.":[127],"Secondly,":[128],"determine":[130],"use":[138],"weighted":[139],"voting":[140],"takes":[142],"learned":[146],"first":[150],"subtask.":[151],"Experimental":[152],"results":[153],"demonstrate":[154],"effectiveness":[156],"our":[158,168],"method":[159],"predicting":[161],"We":[165],"also":[166],"illustrate":[167],"model\u2019s":[169],"interpretability":[170],"through":[171],"comprehensive":[173],"analysis":[174],"experimental":[176],"results.":[177]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
