{"id":"https://openalex.org/W2464272265","doi":"https://doi.org/10.18653/v1/s16-1103","title":"MayoNLP at SemEval-2016 Task 1: Semantic Textual Similarity based on Lexical Semantic Net and Deep Learning Semantic Model","display_name":"MayoNLP at SemEval-2016 Task 1: Semantic Textual Similarity based on Lexical Semantic Net and Deep Learning Semantic Model","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2464272265","doi":"https://doi.org/10.18653/v1/s16-1103","mag":"2464272265"},"language":"en","primary_location":{"id":"doi:10.18653/v1/s16-1103","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s16-1103","pdf_url":"https://www.aclweb.org/anthology/S16-1103.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 10th International Workshop on Semantic Evaluation (SemEval-2016)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/S16-1103.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014602642","display_name":"Naveed Afzal","orcid":"https://orcid.org/0000-0001-8878-0256"},"institutions":[{"id":"https://openalex.org/I4210146710","display_name":"Mayo Clinic in Florida","ror":"https://ror.org/03zzw1w08","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1330342723","https://openalex.org/I4210146710"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Naveed Afzal","raw_affiliation_strings":["Department of Health Sciences Research Mayo Clinic, Rochester, MN"],"affiliations":[{"raw_affiliation_string":"Department of Health Sciences Research Mayo Clinic, Rochester, MN","institution_ids":["https://openalex.org/I4210146710"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080116611","display_name":"Yanshan Wang","orcid":"https://orcid.org/0000-0003-4433-7839"},"institutions":[{"id":"https://openalex.org/I4210146710","display_name":"Mayo Clinic in Florida","ror":"https://ror.org/03zzw1w08","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1330342723","https://openalex.org/I4210146710"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanshan Wang","raw_affiliation_strings":["Department of Health Sciences Research Mayo Clinic, Rochester, MN"],"affiliations":[{"raw_affiliation_string":"Department of Health Sciences Research Mayo Clinic, Rochester, MN","institution_ids":["https://openalex.org/I4210146710"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101585391","display_name":"Hongfang Liu","orcid":"https://orcid.org/0000-0003-2570-3741"},"institutions":[{"id":"https://openalex.org/I4210146710","display_name":"Mayo Clinic in Florida","ror":"https://ror.org/03zzw1w08","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1330342723","https://openalex.org/I4210146710"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hongfang Liu","raw_affiliation_strings":["Department of Health Sciences Research Mayo Clinic, Rochester, MN"],"affiliations":[{"raw_affiliation_string":"Department of Health Sciences Research Mayo Clinic, Rochester, MN","institution_ids":["https://openalex.org/I4210146710"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101585391"],"corresponding_institution_ids":["https://openalex.org/I4210146710"],"apc_list":null,"apc_paid":null,"fwci":7.951,"has_fulltext":true,"cited_by_count":28,"citation_normalized_percentile":{"value":0.97411942,"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":"674","last_page":"679"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T10181","display_name":"Natural Language Processing Techniques","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":0.9957000017166138,"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":0.8405047059059143},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.7754822969436646},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.7706934213638306},{"id":"https://openalex.org/keywords/semeval","display_name":"SemEval","score":0.7627195119857788},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7526425123214722},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6862605810165405},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5527299642562866},{"id":"https://openalex.org/keywords/semantic-memory","display_name":"Semantic memory","score":0.4852449893951416},{"id":"https://openalex.org/keywords/semantic-computing","display_name":"Semantic computing","score":0.4745139479637146},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42801111936569214},{"id":"https://openalex.org/keywords/semantic-web","display_name":"Semantic Web","score":0.17528891563415527},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.051245808601379395}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8405047059059143},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.7754822969436646},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.7706934213638306},{"id":"https://openalex.org/C44572571","wikidata":"https://www.wikidata.org/wiki/Q7448970","display_name":"SemEval","level":3,"score":0.7627195119857788},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7526425123214722},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6862605810165405},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5527299642562866},{"id":"https://openalex.org/C197914299","wikidata":"https://www.wikidata.org/wiki/Q18650","display_name":"Semantic memory","level":3,"score":0.4852449893951416},{"id":"https://openalex.org/C511149849","wikidata":"https://www.wikidata.org/wiki/Q7449051","display_name":"Semantic computing","level":3,"score":0.4745139479637146},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42801111936569214},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.17528891563415527},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.051245808601379395},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","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},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/s16-1103","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s16-1103","pdf_url":"https://www.aclweb.org/anthology/S16-1103.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 10th International Workshop on Semantic Evaluation (SemEval-2016)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/s16-1103","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s16-1103","pdf_url":"https://www.aclweb.org/anthology/S16-1103.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 10th International Workshop on Semantic Evaluation (SemEval-2016)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3595008145","display_name":null,"funder_award_id":"R01LM11934","funder_id":"https://openalex.org/F4320337372","funder_display_name":"U.S. National Library of Medicine"}],"funders":[{"id":"https://openalex.org/F4320337354","display_name":"National Institute of General Medical Sciences","ror":"https://ror.org/04q48ey07"},{"id":"https://openalex.org/F4320337372","display_name":"U.S. National Library of Medicine","ror":"https://ror.org/0060t0j89"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2464272265.pdf","grobid_xml":"https://content.openalex.org/works/W2464272265.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W13343750","https://openalex.org/W1566018662","https://openalex.org/W1746620543","https://openalex.org/W1942015218","https://openalex.org/W1980776243","https://openalex.org/W2012179416","https://openalex.org/W2053968437","https://openalex.org/W2121184547","https://openalex.org/W2126400076","https://openalex.org/W2131876387","https://openalex.org/W2132019450","https://openalex.org/W2133458109","https://openalex.org/W2136189984","https://openalex.org/W2152180407","https://openalex.org/W2161443453","https://openalex.org/W2250645967","https://openalex.org/W2251008987","https://openalex.org/W2251861449","https://openalex.org/W2950225692"],"related_works":["https://openalex.org/W2752041471","https://openalex.org/W78638240","https://openalex.org/W2252122760","https://openalex.org/W1965623300","https://openalex.org/W3134365128","https://openalex.org/W2105461184","https://openalex.org/W2114797768","https://openalex.org/W2380654781","https://openalex.org/W2541135911","https://openalex.org/W2359259132"],"abstract_inverted_index":{"Given":[0],"two":[1,17,52,64],"sentences,":[2],"participating":[3],"systems":[4],"assign":[5],"a":[6],"semantic":[7,28,42],"similarity":[8],"score":[9],"in":[10],"the":[11,21,35,63,69,72,89],"range":[12],"of":[13,78,93],"0-5.":[14],"We":[15,48],"applied":[16],"different":[18],"techniques":[19,65],"for":[20],"task:":[22],"one":[23],"is":[24,37],"based":[25,38],"on":[26,39,75,88],"lexical":[27],"net":[29],"(corresponding":[30,44,55],"to":[31,45,56],"run":[32,46,57],"1)":[33],"and":[34,85,87,96],"other":[36],"deep":[40],"learning":[41],"model":[43],"2).":[47],"also":[49],"combined":[50],"these":[51],"runs":[53],"linearly":[54],"3).":[58],"Our":[59],"results":[60],"indicate":[61],"that":[62],"perform":[66],"comparably":[67],"while":[68],"combination":[70],"outperforms":[71],"individual":[73],"ones":[74],"four":[76],"out":[77],"five":[79],"datasets,":[80],"namely":[81],"answeranswer,":[82],"headlines,":[83],"plagiarism,":[84],"questionquestion,":[86],"overall":[90],"weighted":[91],"mean":[92],"STS":[94],"2016":[95],"2015":[97],"datasets.":[98]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":9},{"year":2016,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
