{"id":"https://openalex.org/W2741164290","doi":"https://doi.org/10.18653/v1/p17-2029","title":"A Two-Stage Parsing Method for Text-Level Discourse Analysis","display_name":"A Two-Stage Parsing Method for Text-Level Discourse Analysis","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2741164290","doi":"https://doi.org/10.18653/v1/p17-2029","mag":"2741164290"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p17-2029","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p17-2029","pdf_url":"https://www.aclweb.org/anthology/P17-2029.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 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)","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/P17-2029.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101621485","display_name":"Yizhong Wang","orcid":"https://orcid.org/0000-0002-9760-3910"},"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":"Yizhong 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/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":"Sujian Li","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":"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"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Houfeng 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"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":6.9212,"has_fulltext":true,"cited_by_count":107,"citation_normalized_percentile":{"value":0.97792263,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"184","last_page":"188"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"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/T10028","display_name":"Topic Modeling","score":1.0,"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/T10181","display_name":"Natural Language Processing Techniques","score":1.0,"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/T12031","display_name":"Speech and dialogue systems","score":0.9980000257492065,"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":0.827947735786438},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.7907605171203613},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6596910357475281},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6435601115226746},{"id":"https://openalex.org/keywords/paragraph","display_name":"Paragraph","score":0.641621470451355},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6258537769317627},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5717600584030151},{"id":"https://openalex.org/keywords/span","display_name":"Span (engineering)","score":0.5662457346916199},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5406595468521118},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5140451192855835},{"id":"https://openalex.org/keywords/transition","display_name":"Transition (genetics)","score":0.4425428509712219},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.10863631963729858},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0838189423084259}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.827947735786438},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.7907605171203613},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6596910357475281},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6435601115226746},{"id":"https://openalex.org/C2777206241","wikidata":"https://www.wikidata.org/wiki/Q194431","display_name":"Paragraph","level":2,"score":0.641621470451355},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6258537769317627},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5717600584030151},{"id":"https://openalex.org/C2778753569","wikidata":"https://www.wikidata.org/wiki/Q1960395","display_name":"Span (engineering)","level":2,"score":0.5662457346916199},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5406595468521118},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5140451192855835},{"id":"https://openalex.org/C194232998","wikidata":"https://www.wikidata.org/wiki/Q1606712","display_name":"Transition (genetics)","level":3,"score":0.4425428509712219},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.10863631963729858},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0838189423084259},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/p17-2029","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p17-2029","pdf_url":"https://www.aclweb.org/anthology/P17-2029.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 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/p17-2029","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p17-2029","pdf_url":"https://www.aclweb.org/anthology/P17-2029.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 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.6600000262260437,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"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/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2741164290.pdf","grobid_xml":"https://content.openalex.org/works/W2741164290.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W181643614","https://openalex.org/W332673702","https://openalex.org/W1597655096","https://openalex.org/W1980062491","https://openalex.org/W2015238682","https://openalex.org/W2038754641","https://openalex.org/W2044599851","https://openalex.org/W2045738181","https://openalex.org/W2120136138","https://openalex.org/W2120262106","https://openalex.org/W2121227244","https://openalex.org/W2123442489","https://openalex.org/W2158139315","https://openalex.org/W2165887782","https://openalex.org/W2167746972","https://openalex.org/W2250767751","https://openalex.org/W2251211118","https://openalex.org/W2251293245","https://openalex.org/W2252267789","https://openalex.org/W2565349465","https://openalex.org/W4250641076","https://openalex.org/W4253555784"],"related_works":["https://openalex.org/W2377059580","https://openalex.org/W2391800119","https://openalex.org/W2595239241","https://openalex.org/W2799181378","https://openalex.org/W2052919063","https://openalex.org/W3003711649","https://openalex.org/W2904173691","https://openalex.org/W4365517254","https://openalex.org/W270947280","https://openalex.org/W2366422761"],"abstract_inverted_index":{"Previous":[0],"work":[1],"introduced":[2],"transition-based":[3,31],"algorithms":[4],"to":[5,48],"form":[6],"a":[7,87],"unified":[8],"architecture":[9],"of":[10,73,76],"parsing":[11,37,90],"rhetorical":[12],"structures":[13],"(including":[14],"span,":[15],"nuclearity":[16,112],"and":[17,45,66,81,111],"relation),":[18],"but":[19],"did":[20],"not":[21],"achieve":[22],"satisfactory":[23],"performance.":[24],"In":[25],"this":[26],"paper,":[27],"we":[28,55,85],"propose":[29],"that":[30,57,102],"model":[32],"is":[33],"more":[34],"appropriate":[35],"for":[36,92],"the":[38,52],"naked":[39,63],"discourse":[40],"tree":[41,64,96],"(i.e.,":[42],"identifying":[43],"span":[44,110],"nuclearity)":[46],"due":[47],"data":[49],"sparsity.":[50],"At":[51],"same":[53],"time,":[54],"argue":[56],"relation":[58],"labeling":[59],"can":[60],"benefit":[61],"from":[62,97],"structure":[65],"should":[67],"be":[68],"treated":[69],"elaborately":[70],"with":[71],"consideration":[72],"three":[74],"kinds":[75],"relations":[77],"including":[78],"within-sentence,":[79],"across-sentence":[80],"across-paragraph":[82],"relations.":[83],"Thus,":[84],"design":[86],"pipelined":[88],"two-stage":[89],"method":[91,104],"generating":[93],"an":[94],"RST":[95],"text.":[98],"Experimental":[99],"results":[100],"show":[101],"our":[103],"achieves":[105],"state-of-the-art":[106],"performance,":[107],"especially":[108],"on":[109],"identification.":[113]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":29},{"year":2020,"cited_by_count":20},{"year":2019,"cited_by_count":17},{"year":2018,"cited_by_count":10},{"year":2017,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
