{"id":"https://openalex.org/W2460407294","doi":"https://doi.org/10.18653/v1/s16-1093","title":"NaCTeM at SemEval-2016 Task 1: Inferring sentence-level semantic similarity from an ensemble of complementary lexical and sentence-level features","display_name":"NaCTeM at SemEval-2016 Task 1: Inferring sentence-level semantic similarity from an ensemble of complementary lexical and sentence-level features","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2460407294","doi":"https://doi.org/10.18653/v1/s16-1093","mag":"2460407294"},"language":"en","primary_location":{"id":"doi:10.18653/v1/s16-1093","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s16-1093","pdf_url":"https://www.aclweb.org/anthology/S16-1093.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/S16-1093.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024181945","display_name":"Piotr Przyby\u0142a","orcid":"https://orcid.org/0000-0001-9043-6817"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Piotr Przyby\u0142a","raw_affiliation_strings":["National Centre for Text Mining University of Manchester Manchester, UK"],"affiliations":[{"raw_affiliation_string":"National Centre for Text Mining University of Manchester Manchester, UK","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050147609","display_name":"Nhung T. H. Nguyen","orcid":"https://orcid.org/0000-0001-5935-9235"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Nhung T. H. Nguyen","raw_affiliation_strings":["National Centre for Text Mining University of Manchester Manchester, UK"],"affiliations":[{"raw_affiliation_string":"National Centre for Text Mining University of Manchester Manchester, UK","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035746903","display_name":"Matthew Shardlow","orcid":"https://orcid.org/0000-0003-1129-2750"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Matthew Shardlow","raw_affiliation_strings":["National Centre for Text Mining University of Manchester Manchester, UK"],"affiliations":[{"raw_affiliation_string":"National Centre for Text Mining University of Manchester Manchester, UK","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077625160","display_name":"Georgios Kontonatsios","orcid":"https://orcid.org/0000-0001-5935-4709"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Georgios Kontonatsios","raw_affiliation_strings":["National Centre for Text Mining University of Manchester Manchester, UK"],"affiliations":[{"raw_affiliation_string":"National Centre for Text Mining University of Manchester Manchester, UK","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077976343","display_name":"Sophia Ananiadou","orcid":"https://orcid.org/0000-0002-4097-9191"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Sophia Ananiadou","raw_affiliation_strings":["National Centre for Text Mining University of Manchester Manchester, UK"],"affiliations":[{"raw_affiliation_string":"National Centre for Text Mining University of Manchester Manchester, UK","institution_ids":["https://openalex.org/I28407311"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5024181945"],"corresponding_institution_ids":["https://openalex.org/I28407311"],"apc_list":null,"apc_paid":null,"fwci":1.7669,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.89136079,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"614","last_page":"620"},"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":0.9998999834060669,"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":0.8484050035476685},{"id":"https://openalex.org/keywords/semeval","display_name":"SemEval","score":0.8402290344238281},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.7408813238143921},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.7122011184692383},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.695962131023407},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.6931052803993225},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6613160967826843},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6261917948722839},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.5203649401664734}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8484050035476685},{"id":"https://openalex.org/C44572571","wikidata":"https://www.wikidata.org/wiki/Q7448970","display_name":"SemEval","level":3,"score":0.8402290344238281},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.7408813238143921},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7122011184692383},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.695962131023407},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.6931052803993225},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6613160967826843},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6261917948722839},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.5203649401664734},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/v1/s16-1093","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s16-1093","pdf_url":"https://www.aclweb.org/anthology/S16-1093.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"},{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/681463b9-1bd9-4179-bc75-fcd7044b9c2b","is_oa":true,"landing_page_url":"https://research.manchester.ac.uk/en/publications/681463b9-1bd9-4179-bc75-fcd7044b9c2b","pdf_url":"https://aclanthology.org/S16-1093.pdf","source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Przybyla, P, Nguyen, N, Shardlow, M, Kontonatsios, G & Ananiadou, S 2016, NaCTeM at SemEval-2016 Task 1: Inferring sentence-level semantic similarity from an ensemble of complementary lexical and sentence-level features. in Proceedings of the 10th International Workshop on Semantic Evaluation . < http://www.aclweb.org/anthology/S16-1093 >","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:pure.atira.dk:publications/681463b9-1bd9-4179-bc75-fcd7044b9c2b","is_oa":true,"landing_page_url":"http://www.aclweb.org/anthology/S16-1093","pdf_url":null,"source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""}],"best_oa_location":{"id":"doi:10.18653/v1/s16-1093","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s16-1093","pdf_url":"https://www.aclweb.org/anthology/S16-1093.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":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2460407294.pdf","grobid_xml":"https://content.openalex.org/works/W2460407294.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W1566018662","https://openalex.org/W1594031697","https://openalex.org/W1614298861","https://openalex.org/W1647671624","https://openalex.org/W1662133657","https://openalex.org/W1748393397","https://openalex.org/W1880262756","https://openalex.org/W1984052055","https://openalex.org/W2038721957","https://openalex.org/W2100935296","https://openalex.org/W2101105183","https://openalex.org/W2102201073","https://openalex.org/W2115127435","https://openalex.org/W2120779048","https://openalex.org/W2123442489","https://openalex.org/W2124379907","https://openalex.org/W2131151781","https://openalex.org/W2133458109","https://openalex.org/W2133459682","https://openalex.org/W2136930489","https://openalex.org/W2149327368","https://openalex.org/W2251044566","https://openalex.org/W2251695880","https://openalex.org/W2251780596","https://openalex.org/W2251803266","https://openalex.org/W2462305634","https://openalex.org/W2582743722","https://openalex.org/W2911964244","https://openalex.org/W2950577311","https://openalex.org/W3014310718","https://openalex.org/W4231510805","https://openalex.org/W4249303080","https://openalex.org/W4255386104","https://openalex.org/W4297957988","https://openalex.org/W4301429599"],"related_works":["https://openalex.org/W3029012650","https://openalex.org/W346416711","https://openalex.org/W2752041471","https://openalex.org/W78638240","https://openalex.org/W2252122760","https://openalex.org/W2105461184","https://openalex.org/W2114797768","https://openalex.org/W2380654781","https://openalex.org/W2176214140","https://openalex.org/W2516873349"],"abstract_inverted_index":{"We":[0,34],"present":[1],"a":[2,43,52,61,80,101,109],"description":[3],"of":[4,54,88,111],"the":[5,9,24,31,74,86,97,114,122],"system":[6],"submitted":[7],"to":[8,22,26,40,78],"Semantic":[10],"Textual":[11],"Similarity":[12,76],"(STS)":[13],"shared":[14],"task":[15,20],"at":[16],"SemEval":[17],"2016.":[18],"The":[19,48],"is":[21],"assess":[23,85],"degree":[25],"which":[27],"two":[28,37,98],"sentences":[29],"carry":[30],"same":[32],"meaning.":[33],"have":[35],"designed":[36],"different":[38],"methods":[39,99],"automatically":[41],"compute":[42],"similarity":[44,56],"score":[45,110],"between":[46],"sentences.":[47],"first":[49],"method":[50],"combines":[51],"variety":[53],"semantic":[55],"measures":[57],"as":[58],"features":[59],"in":[60],"machine":[62],"learning":[63],"model.":[64,104],"In":[65],"our":[66],"second":[67],"approach,":[68],"we":[69,95],"employ":[70],"training":[71],"data":[72,118],"from":[73],"Interpretable":[75],"subtask":[77],"create":[79],"combined":[81],"wordsimilarity":[82],"measure":[83],"and":[84,91,119],"importance":[87],"both":[89],"aligned":[90],"unaligned":[92],"words.":[93],"Finally,":[94],"combine":[96],"into":[100],"single":[102],"hybrid":[103],"Our":[105],"best-performing":[106],"run":[107],"attains":[108],"0.7732":[112],"on":[113,121],"2015":[115],"STS":[116,124],"evaluation":[117,125],"0.7488":[120],"2016":[123],"data.":[126]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-04-06T07:47:59.780226","created_date":"2025-10-10T00:00:00"}