{"id":"https://openalex.org/W2758753219","doi":"https://doi.org/10.18653/v1/d17-1139","title":"Learning to Rank Semantic Coherence for Topic Segmentation","display_name":"Learning to Rank Semantic Coherence for Topic Segmentation","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2758753219","doi":"https://doi.org/10.18653/v1/d17-1139","mag":"2758753219"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d17-1139","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d17-1139","pdf_url":"https://www.aclweb.org/anthology/D17-1139.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 2017 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D17-1139.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100456546","display_name":"Liang Wang","orcid":"https://orcid.org/0000-0002-6112-1928"},"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":"Liang Wang","raw_affiliation_strings":["Key Laboratory of Computational Linguistics, Peking University, MOE, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Computational Linguistics, Peking University, MOE, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058353424","display_name":"Sujian Li","orcid":"https://orcid.org/0000-0001-7493-0786"},"institutions":[{"id":"https://openalex.org/I4210157323","display_name":"South China Institute of Collaborative Innovation","ror":"https://ror.org/04jnpk588","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210157323","https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sujian Li","raw_affiliation_strings":["Collaborative Innovation Center for Language Ability, Xuzhou, Jiangsu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Collaborative Innovation Center for Language Ability, Xuzhou, Jiangsu, China","institution_ids":["https://openalex.org/I4210157323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109031521","display_name":"Yajuan Lv","orcid":null},"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"]},{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yajuan Lv","raw_affiliation_strings":["Key Laboratory of Computational Linguistics, Peking University, MOE, China","Baidu Inc., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Computational Linguistics, Peking University, MOE, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025565222","display_name":"Houfeng Wang","orcid":"https://orcid.org/0000-0001-7130-1589"},"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"]},{"id":"https://openalex.org/I4210157323","display_name":"South China Institute of Collaborative Innovation","ror":"https://ror.org/04jnpk588","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210157323","https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Houfeng WANG","raw_affiliation_strings":["Collaborative Innovation Center for Language Ability, Xuzhou, Jiangsu, China","Key Laboratory of Computational Linguistics, Peking University, MOE, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Collaborative Innovation Center for Language Ability, Xuzhou, Jiangsu, China","institution_ids":["https://openalex.org/I4210157323"]},{"raw_affiliation_string":"Key Laboratory of Computational Linguistics, Peking University, MOE, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1340","last_page":"1344"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9991999864578247,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9969000220298767,"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":0.836968183517456},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.7520726919174194},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.7325409650802612},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6863179206848145},{"id":"https://openalex.org/keywords/coherence","display_name":"Coherence (philosophical gambling strategy)","score":0.6765758395195007},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6323027610778809},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6145055890083313},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.5008454322814941},{"id":"https://openalex.org/keywords/text-segmentation","display_name":"Text segmentation","score":0.43819066882133484},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4172223210334778},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3651084303855896},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.09681621193885803},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.071135014295578},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.05953380465507507}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.836968183517456},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.7520726919174194},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.7325409650802612},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6863179206848145},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.6765758395195007},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6323027610778809},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6145055890083313},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.5008454322814941},{"id":"https://openalex.org/C98501671","wikidata":"https://www.wikidata.org/wiki/Q1948408","display_name":"Text segmentation","level":3,"score":0.43819066882133484},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4172223210334778},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3651084303855896},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.09681621193885803},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.071135014295578},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.05953380465507507},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/d17-1139","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d17-1139","pdf_url":"https://www.aclweb.org/anthology/D17-1139.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 2017 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/d17-1139","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d17-1139","pdf_url":"https://www.aclweb.org/anthology/D17-1139.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 2017 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.4099999964237213}],"awards":[{"id":"https://openalex.org/G2204133011","display_name":"\u6c49\u8bed\u591a\u5c42\u6b21\u8bed\u7bc7\u5206\u6790\u7406\u8bba\u65b9\u6cd5\u7814\u7a76\u4e0e\u5e94\u7528","funder_award_id":"61333018","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G633047379","display_name":"\u9762\u5411\u79d1\u6280\u6587\u732e\u7684\u5f15\u7528\u6458\u8981\u751f\u6210\u5173\u952e\u6280\u672f\u7814\u7a76","funder_award_id":"61572049","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320318547","display_name":"Baidu","ror":"https://ror.org/03vs3wt56"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320324787","display_name":"Peking University","ror":"https://ror.org/02v51f717"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2758753219.pdf","grobid_xml":"https://content.openalex.org/works/W2758753219.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1557074680","https://openalex.org/W1626945812","https://openalex.org/W1828401780","https://openalex.org/W1862888253","https://openalex.org/W1966443646","https://openalex.org/W1973435495","https://openalex.org/W2027823133","https://openalex.org/W2067002391","https://openalex.org/W2101626488","https://openalex.org/W2128709346","https://openalex.org/W2128892113","https://openalex.org/W2144092076","https://openalex.org/W2159083595","https://openalex.org/W2167055684","https://openalex.org/W2170738476","https://openalex.org/W2211192759","https://openalex.org/W2250539671","https://openalex.org/W2397462379","https://openalex.org/W2951359136"],"related_works":["https://openalex.org/W579810227","https://openalex.org/W2952780262","https://openalex.org/W2979495269","https://openalex.org/W2392917763","https://openalex.org/W2083429127","https://openalex.org/W2358855848","https://openalex.org/W2142145894","https://openalex.org/W2807110902","https://openalex.org/W2095908250","https://openalex.org/W2143806604"],"abstract_inverted_index":{"Topic":[0],"segmentation":[1],"plays":[2],"an":[3,39],"important":[4],"role":[5],"for":[6,31],"discourse":[7],"parsing":[8],"and":[9,41,84],"information":[10],"retrieval.":[11],"Due":[12],"to":[13,25,44,80,92,102],"the":[14,60,70,86,90],"absence":[15],"of":[16,56],"training":[17,49,71],"data,":[18],"previous":[19],"work":[20],"mainly":[21],"adopts":[22],"unsupervised":[23],"methods":[24],"rank":[26,85,93],"semantic":[27,67,87],"coherence":[28,88],"between":[29],"paragraphs":[30],"topic":[32],"segmentation.":[33],"In":[34],"this":[35],"paper,":[36],"we":[37,73],"present":[38],"intuitive":[40],"simple":[42],"idea":[43],"automatically":[45],"create":[46],"a":[47,53,75],"\u201cquasi\u201d":[48],"dataset,":[50],"which":[51],"includes":[52],"large":[54],"amount":[55],"text":[57,82],"pairs":[58,83],"from":[59],"same":[61],"or":[62],"different":[63,66],"documents":[64],"with":[65],"coherence.":[68],"With":[69],"corpus,":[72],"design":[74],"symmetric":[76],"CNN":[77],"neural":[78],"network":[79],"model":[81],"within":[89],"learning":[91],"framework.":[94],"Experiments":[95],"show":[96],"that":[97],"our":[98],"algorithm":[99],"is":[100],"able":[101],"achieve":[103],"competitive":[104],"performance":[105],"over":[106],"strong":[107],"baselines":[108],"on":[109],"several":[110],"real-world":[111],"datasets.":[112]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}