{"id":"https://openalex.org/W2250965765","doi":"https://doi.org/10.3115/v1/w14-4327","title":"Reducing Sparsity Improves the Recognition of Implicit Discourse Relations","display_name":"Reducing Sparsity Improves the Recognition of Implicit Discourse Relations","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W2250965765","doi":"https://doi.org/10.3115/v1/w14-4327","mag":"2250965765"},"language":"en","primary_location":{"id":"doi:10.3115/v1/w14-4327","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/w14-4327","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 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3115/v1/w14-4327","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021057186","display_name":"Junyi Jessy Li","orcid":"https://orcid.org/0000-0002-2550-5262"},"institutions":[{"id":"https://openalex.org/I36788626","display_name":"California University of Pennsylvania","ror":"https://ror.org/01spssf70","country_code":"US","type":"education","lineage":["https://openalex.org/I36788626"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Junyi Jessy Li","raw_affiliation_strings":["University of Pennsylvania"],"affiliations":[{"raw_affiliation_string":"University of Pennsylvania","institution_ids":["https://openalex.org/I36788626"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032571629","display_name":"Ani Nenkova","orcid":"https://orcid.org/0000-0002-5825-7875"},"institutions":[{"id":"https://openalex.org/I36788626","display_name":"California University of Pennsylvania","ror":"https://ror.org/01spssf70","country_code":"US","type":"education","lineage":["https://openalex.org/I36788626"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ani Nenkova","raw_affiliation_strings":["University of Pennsylvania"],"affiliations":[{"raw_affiliation_string":"University of Pennsylvania","institution_ids":["https://openalex.org/I36788626"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5021057186"],"corresponding_institution_ids":["https://openalex.org/I36788626"],"apc_list":null,"apc_paid":null,"fwci":6.3428,"has_fulltext":true,"cited_by_count":20,"citation_normalized_percentile":{"value":0.96601856,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"199","last_page":"207"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9993000030517578,"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/T13629","display_name":"Text Readability and Simplification","score":0.9979000091552734,"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":0.8273930549621582},{"id":"https://openalex.org/keywords/lexicalization","display_name":"Lexicalization","score":0.7989376783370972},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6942852735519409},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6732820868492126},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.63356614112854},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6211578845977783},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6144920587539673},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5649265646934509},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.49505892395973206},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.4713199734687805},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.2523968517780304},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.07085248827934265}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8273930549621582},{"id":"https://openalex.org/C2777532361","wikidata":"https://www.wikidata.org/wiki/Q687185","display_name":"Lexicalization","level":2,"score":0.7989376783370972},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6942852735519409},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6732820868492126},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.63356614112854},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6211578845977783},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6144920587539673},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5649265646934509},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.49505892395973206},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4713199734687805},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.2523968517780304},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.07085248827934265},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3115/v1/w14-4327","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/w14-4327","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 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.675.8813","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.675.8813","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://anthology.aclweb.org/W/W14/W14-4327.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.675.9082","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.675.9082","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cis.upenn.edu/%7Eljunyi/papers/impdisc_sparsity.pdf","raw_type":"text"}],"best_oa_location":{"id":"doi:10.3115/v1/w14-4327","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/w14-4327","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 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W29849901","https://openalex.org/W102059219","https://openalex.org/W1541881462","https://openalex.org/W1563792215","https://openalex.org/W1574862351","https://openalex.org/W1632114991","https://openalex.org/W2040960947","https://openalex.org/W2109462987","https://openalex.org/W2115629999","https://openalex.org/W2124169982","https://openalex.org/W2131861279","https://openalex.org/W2151873717","https://openalex.org/W2152197045","https://openalex.org/W2166957049"],"related_works":["https://openalex.org/W4313416158","https://openalex.org/W2381724108","https://openalex.org/W2371809621","https://openalex.org/W2381174322","https://openalex.org/W2406713797","https://openalex.org/W2366883429","https://openalex.org/W2370516645","https://openalex.org/W2374198046","https://openalex.org/W2563379650","https://openalex.org/W2349112121"],"abstract_inverted_index":{"The":[0],"earliest":[1],"work":[2],"on":[3,11,55,122],"automatic":[4],"detec-tion":[5],"of":[6,38,40,74,91],"implicit":[7],"discourse":[8,113],"relations":[9],"relied":[10],"lexical":[12,25,126],"features.":[13,50],"More":[14],"recently,":[15],"re-searchers":[16],"have":[17],"demonstrated":[18],"that":[19,65,86,89,119],"syntactic":[20,44,93,106],"features":[21,26,75,94],"are":[22],"superior":[23],"to":[24,58,110],"for":[27,70,97],"the":[28,35,41,56,71,92],"task.":[29],"In":[30,51],"this":[31],"paper":[32],"we":[33,53,117],"re-examine":[34],"two":[36],"classes":[37],"state":[39],"art":[42],"representa-tions:":[43],"production":[45],"rules":[46],"and":[47,80,131],"word":[48],"pair":[49],"particular,":[52],"focus":[54],"need":[57],"reduce":[59,81],"sparsity":[60,78],"in":[61,112,137],"instance":[62],"repre-sentation,":[63],"demonstrating":[64],"different":[66,123],"rep-resentation":[67],"choices":[68],"even":[69],"same":[72],"class":[73],"may":[76],"exacerbate":[77],"issues":[79],"performance.":[82,99],"We":[83,100],"present":[84],"re-sults":[85],"clearly":[87],"reveal":[88],"lexicalization":[90],"is":[95],"necessary":[96],"good":[98],"introduce":[101],"a":[102],"novel,":[103],"less":[104],"sparse,":[105],"representation":[107],"which":[108],"leads":[109],"improvement":[111],"rela-tion":[114],"recognition.":[115],"Finally,":[116],"demonstrate":[118],"classifiers":[120],"trained":[121],"repre-sentations,":[124],"especially":[125],"ones,":[127],"behave":[128],"rather":[129],"differently":[130],"thus":[132],"could":[133],"likely":[134],"be":[135],"combined":[136],"future":[138],"systems.":[139],"1":[140]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
