{"id":"https://openalex.org/W3101427524","doi":"https://doi.org/10.18653/v1/2020.emnlp-main.140","title":"Incorporating Multimodal Information in Open-Domain Web Keyphrase Extraction","display_name":"Incorporating Multimodal Information in Open-Domain Web Keyphrase Extraction","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3101427524","doi":"https://doi.org/10.18653/v1/2020.emnlp-main.140","mag":"3101427524"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2020.emnlp-main.140","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2020.emnlp-main.140","pdf_url":"https://www.aclweb.org/anthology/2020.emnlp-main.140.pdf","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 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/2020.emnlp-main.140.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030640229","display_name":"Yansen Wang","orcid":"https://orcid.org/0000-0002-4867-1503"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yansen Wang","raw_affiliation_strings":["Language Technologies Institute, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh PA, 15213"],"affiliations":[{"raw_affiliation_string":"Language Technologies Institute, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh PA, 15213","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101559598","display_name":"Zhen Fan","orcid":"https://orcid.org/0000-0001-6196-1057"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhen Fan","raw_affiliation_strings":["Language Technologies Institute, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh PA, 15213"],"affiliations":[{"raw_affiliation_string":"Language Technologies Institute, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh PA, 15213","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089539629","display_name":"Carolyn Penstein Ros\u00e9","orcid":"https://orcid.org/0000-0003-1128-5155"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Carolyn Rose","raw_affiliation_strings":["Language Technologies Institute, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh PA, 15213"],"affiliations":[{"raw_affiliation_string":"Language Technologies Institute, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh PA, 15213","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5030640229"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":1.4582,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.86158947,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1790","last_page":"1800"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.8280432224273682},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6228812336921692},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5341346263885498},{"id":"https://openalex.org/keywords/disk-formatting","display_name":"Disk formatting","score":0.45999324321746826},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4565843939781189},{"id":"https://openalex.org/keywords/web-page","display_name":"Web page","score":0.44951725006103516},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.42480576038360596},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40959644317626953},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.23253506422042847}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8280432224273682},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6228812336921692},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5341346263885498},{"id":"https://openalex.org/C88006597","wikidata":"https://www.wikidata.org/wiki/Q690117","display_name":"Disk formatting","level":2,"score":0.45999324321746826},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4565843939781189},{"id":"https://openalex.org/C21959979","wikidata":"https://www.wikidata.org/wiki/Q36774","display_name":"Web page","level":2,"score":0.44951725006103516},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.42480576038360596},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40959644317626953},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.23253506422042847},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2020.emnlp-main.140","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2020.emnlp-main.140","pdf_url":"https://www.aclweb.org/anthology/2020.emnlp-main.140.pdf","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 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2020.emnlp-main.140","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2020.emnlp-main.140","pdf_url":"https://www.aclweb.org/anthology/2020.emnlp-main.140.pdf","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 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3101427524.pdf","grobid_xml":"https://content.openalex.org/works/W3101427524.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W1490343430","https://openalex.org/W1492250428","https://openalex.org/W1724344851","https://openalex.org/W1983873791","https://openalex.org/W2030903088","https://openalex.org/W2045181608","https://openalex.org/W2064418625","https://openalex.org/W2102733276","https://openalex.org/W2106867392","https://openalex.org/W2123107811","https://openalex.org/W2136075087","https://openalex.org/W2145049651","https://openalex.org/W2156577800","https://openalex.org/W2163659824","https://openalex.org/W2167329753","https://openalex.org/W2194775991","https://openalex.org/W2783786042","https://openalex.org/W2888766462","https://openalex.org/W2896457183","https://openalex.org/W2914076857","https://openalex.org/W2930279335","https://openalex.org/W2951534261","https://openalex.org/W2962739339","https://openalex.org/W2963265326","https://openalex.org/W2963341956","https://openalex.org/W2963403868","https://openalex.org/W2970467549","https://openalex.org/W2970971581","https://openalex.org/W2980282514","https://openalex.org/W2980598790","https://openalex.org/W3023787386","https://openalex.org/W4285719527","https://openalex.org/W4295312788","https://openalex.org/W4297805475","https://openalex.org/W4385245566"],"related_works":["https://openalex.org/W4244466418","https://openalex.org/W2104062382","https://openalex.org/W2162878363","https://openalex.org/W2389021890","https://openalex.org/W3023343521","https://openalex.org/W4245101192","https://openalex.org/W2146588098","https://openalex.org/W2479325685","https://openalex.org/W3183791698","https://openalex.org/W3111710556"],"abstract_inverted_index":{"Open-domain":[0],"Keyphrase":[1],"extraction":[2],"(KPE)":[3],"on":[4],"the":[5,22,48,57,79,96,134,150],"Web":[6],"is":[7],"a":[8,15,68,110,121],"fundamental":[9],"yet":[10],"complex":[11],"NLP":[12],"task":[13,136],"with":[14],"wide":[16],"range":[17],"of":[18,24,123],"practical":[19],"applications":[20],"within":[21,47,130,149],"field":[23],"Information":[25],"Retrieval.":[26],"In":[27,63,82],"contrast":[28],"to":[29,55,76,98,112],"other":[30],"document":[31],"types,":[32],"web":[33],"page":[34],"designs":[35,45],"are":[36],"intended":[37],"for":[38,133],"easy":[39],"navigation":[40],"and":[41,50,88,103,127],"information":[42,59],"finding.":[43],"Effective":[44],"encode":[46],"layout":[49],"formatting":[51],"signals":[52,75],"that":[53,71,106,120],"point":[54],"where":[56],"important":[58],"can":[60],"be":[61],"found.":[62],"this":[64,131],"work,":[65],"we":[66,84],"propose":[67],"modeling":[69],"approach":[70,132],"leverages":[72],"these":[73,146],"multi-modal":[74],"aid":[77,113],"in":[78,114],"KPE":[80,135],"task.":[81],"particular,":[83],"leverage":[85],"both":[86],"lexical":[87],"visual":[89],"features":[90,105,147],"(e.g.,":[91],"size,":[92],"font,":[93],"position)":[94],"at":[95,109],"micro-level":[97],"enable":[99],"effective":[100,124],"strategy":[101,115,125,128],"induction,":[102],"metalevel":[104],"describe":[107],"pages":[108],"macrolevel":[111],"selection.":[116],"Our":[117],"evaluation":[118],"demonstrates":[119],"combination":[122],"induction":[126],"selection":[129],"outperforms":[137],"state-of-the-art":[138],"models.":[139],"A":[140],"qualitative":[141],"post-hoc":[142],"analysis":[143],"illustrates":[144],"how":[145],"function":[148],"model.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2026-02-03T00:53:05.648605","created_date":"2025-10-10T00:00:00"}