{"id":"https://openalex.org/W2561311854","doi":"https://doi.org/10.18653/v1/d16-1237","title":"Exploiting Sentence Similarities for Better Alignments","display_name":"Exploiting Sentence Similarities for Better Alignments","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2561311854","doi":"https://doi.org/10.18653/v1/d16-1237","mag":"2561311854"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d16-1237","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d16-1237","pdf_url":"https://www.aclweb.org/anthology/D16-1237.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 2016 Conference on Empirical Methods in Natural\n          Language Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D16-1237.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100440801","display_name":"Tao Li","orcid":"https://orcid.org/0000-0001-9968-2993"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tao Li","raw_affiliation_strings":["University of Utah"],"affiliations":[{"raw_affiliation_string":"University of Utah","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013135203","display_name":"Vivek Srikumar","orcid":"https://orcid.org/0000-0003-0419-6568"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vivek Srikumar","raw_affiliation_strings":["University of Utah"],"affiliations":[{"raw_affiliation_string":"University of Utah","institution_ids":["https://openalex.org/I223532165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100440801"],"corresponding_institution_ids":["https://openalex.org/I223532165"],"apc_list":null,"apc_paid":null,"fwci":2.1424,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.90961562,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2193","last_page":"2203"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9842000007629395,"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":0.7244457602500916},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5759581327438354},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4813407361507416},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47774332761764526}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7244457602500916},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5759581327438354},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4813407361507416},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47774332761764526}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/d16-1237","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d16-1237","pdf_url":"https://www.aclweb.org/anthology/D16-1237.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 2016 Conference on Empirical Methods in Natural\n          Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/d16-1237","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d16-1237","pdf_url":"https://www.aclweb.org/anthology/D16-1237.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 2016 Conference on Empirical Methods in Natural\n          Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.5699999928474426,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2561311854.pdf","grobid_xml":"https://content.openalex.org/works/W2561311854.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W112188623","https://openalex.org/W1536616638","https://openalex.org/W1566346388","https://openalex.org/W1647729745","https://openalex.org/W1811404221","https://openalex.org/W1816599501","https://openalex.org/W1980776243","https://openalex.org/W1986069434","https://openalex.org/W2006969979","https://openalex.org/W2027155840","https://openalex.org/W2099884836","https://openalex.org/W2105644991","https://openalex.org/W2105842272","https://openalex.org/W2116679574","https://openalex.org/W2117805756","https://openalex.org/W2121495183","https://openalex.org/W2123442489","https://openalex.org/W2133458109","https://openalex.org/W2153652827","https://openalex.org/W2153653739","https://openalex.org/W2154474435","https://openalex.org/W2156985047","https://openalex.org/W2162130683","https://openalex.org/W2164413279","https://openalex.org/W2165979181","https://openalex.org/W2179862035","https://openalex.org/W2185606683","https://openalex.org/W2250539671","https://openalex.org/W2250825496","https://openalex.org/W2251044566","https://openalex.org/W2251861449","https://openalex.org/W2251896332","https://openalex.org/W2462305634","https://openalex.org/W2464186148","https://openalex.org/W2470838903","https://openalex.org/W2474792830","https://openalex.org/W2534712034","https://openalex.org/W2950225692","https://openalex.org/W2951299559","https://openalex.org/W4211148418","https://openalex.org/W4241645538"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W2350741829","https://openalex.org/W2530322880","https://openalex.org/W3204019825"],"abstract_inverted_index":{"We":[0],"study":[1],"the":[2,24,27,34],"problem":[3],"of":[4,26,37,62,72],"jointly":[5,59],"aligning":[6],"sentence":[7,14,43,74],"constituents":[8,61],"and":[9,22,65,75,90],"predicting":[10,88],"their":[11,68],"similarities.While":[12],"extensive":[13],"similarity":[15],"data":[16,45],"exists,":[17],"manually":[18],"generating":[19],"reference":[20],"alignments":[21,89],"labeling":[23],"similarities":[25],"aligned":[28],"chunks":[29],"is":[30],"comparatively":[31],"onerous.This":[32],"prompts":[33],"natural":[35],"question":[36],"whether":[38],"we":[39,52,79],"can":[40],"exploit":[41],"easy-to-create":[42],"level":[44,77],"to":[46,58],"train":[47],"better":[48],"aligners.In":[49],"this":[50],"paper,":[51],"present":[53],"a":[54],"model":[55,83],"that":[56,81],"learns":[57],"align":[60],"two":[63],"sentences":[64],"also":[66],"predict":[67],"similarities.By":[69],"taking":[70],"advantage":[71],"both":[73],"constituent":[76,91],"data,":[78],"show":[80],"our":[82],"achieves":[84],"state-of-the-art":[85],"performance":[86],"at":[87],"similarities.":[92]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
