{"id":"https://openalex.org/W2806151258","doi":"https://doi.org/10.18653/v1/s18-1170","title":"UMD at SemEval-2018 Task 10: Can Word Embeddings Capture Discriminative Attributes?","display_name":"UMD at SemEval-2018 Task 10: Can Word Embeddings Capture Discriminative Attributes?","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2806151258","doi":"https://doi.org/10.18653/v1/s18-1170","mag":"2806151258"},"language":"en","primary_location":{"id":"doi:10.18653/v1/s18-1170","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s18-1170","pdf_url":"https://www.aclweb.org/anthology/S18-1170.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 12th International Workshop on Semantic Evaluation","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/S18-1170.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066554248","display_name":"Alexander Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexander Zhang","raw_affiliation_strings":["Department of Computer Science University of Maryland College Park, MD 20742, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science University of Maryland College Park, MD 20742, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078390032","display_name":"Marine Carpuat","orcid":"https://orcid.org/0000-0003-1693-0782"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Marine Carpuat","raw_affiliation_strings":["Department of Computer Science University of Maryland College Park, MD 20742, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science University of Maryland College Park, MD 20742, USA","institution_ids":["https://openalex.org/I66946132"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5078390032"],"corresponding_institution_ids":["https://openalex.org/I66946132"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1022","last_page":"1026"},"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":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":0.9970999956130981,"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/discriminative-model","display_name":"Discriminative model","score":0.8263639807701111},{"id":"https://openalex.org/keywords/semeval","display_name":"SemEval","score":0.8044083118438721},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7891691327095032},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.7462464570999146},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7068285942077637},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6636300086975098},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6175340414047241},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5887101888656616},{"id":"https://openalex.org/keywords/cosine-similarity","display_name":"Cosine similarity","score":0.5786914229393005},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.5537796020507812},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5316252112388611},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.501300573348999},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.44988328218460083},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.4320472478866577},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.419845849275589},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3869297206401825},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11715048551559448},{"id":"https://openalex.org/keywords/arithmetic","display_name":"Arithmetic","score":0.05882391333580017}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8263639807701111},{"id":"https://openalex.org/C44572571","wikidata":"https://www.wikidata.org/wiki/Q7448970","display_name":"SemEval","level":3,"score":0.8044083118438721},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7891691327095032},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.7462464570999146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7068285942077637},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6636300086975098},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6175340414047241},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5887101888656616},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.5786914229393005},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.5537796020507812},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5316252112388611},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.501300573348999},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.44988328218460083},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4320472478866577},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.419845849275589},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3869297206401825},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11715048551559448},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.05882391333580017},{"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},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"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/s18-1170","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s18-1170","pdf_url":"https://www.aclweb.org/anthology/S18-1170.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 12th International Workshop on Semantic Evaluation","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/s18-1170","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s18-1170","pdf_url":"https://www.aclweb.org/anthology/S18-1170.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 12th International Workshop on Semantic Evaluation","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7699999809265137}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2806151258.pdf","grobid_xml":"https://content.openalex.org/works/W2806151258.grobid-xml"},"referenced_works_count":12,"referenced_works":["https://openalex.org/W205765513","https://openalex.org/W1583820951","https://openalex.org/W1614298861","https://openalex.org/W2078894097","https://openalex.org/W2141599568","https://openalex.org/W2250539671","https://openalex.org/W2251803266","https://openalex.org/W2517095220","https://openalex.org/W2807304112","https://openalex.org/W2950577311","https://openalex.org/W2962795068","https://openalex.org/W3105971000"],"related_works":["https://openalex.org/W2469576572","https://openalex.org/W2753242182","https://openalex.org/W2805837477","https://openalex.org/W4287890639","https://openalex.org/W4289785748","https://openalex.org/W3117973240","https://openalex.org/W4388925068","https://openalex.org/W3117847293","https://openalex.org/W3115494066","https://openalex.org/W4287663240"],"abstract_inverted_index":{"We":[0,87],"describe":[1],"the":[2,24],"University":[3],"of":[4,85,102,108],"Maryland's":[5],"submission":[6,58],"to":[7,27,36,47],"SemEval-018":[8],"Task":[9],"10,":[10],"\"Capturing":[11],"Discriminative":[12],"Attributes\":":[13],"given":[14],"word":[15,50,68],"triples":[16],"(w":[17],"1":[18,38],",":[19,22],"w":[20,37,41],"2":[21,42],"d),":[23],"goal":[25],"is":[26,31],"determine":[28,48],"whether":[29,49],"d":[30],"a":[32,72],"discriminating":[33],"attribute":[34],"belonging":[35],"but":[39],"not":[40],".":[43],"Our":[44,57],"study":[45],"aims":[46],"embeddings":[51],"can":[52],"address":[53],"this":[54,60],"challenging":[55],"task.":[56],"casts":[59],"problem":[61],"as":[62],"supervised":[63,105],"binary":[64],"classification":[65],"using":[66],"only":[67,77],"embedding":[69],"features.":[70],"Using":[71],"gaussian":[73],"SVM":[74],"model":[75],"trained":[76],"on":[78],"validation":[79],"data":[80],"results":[81],"in":[82,98],"an":[83],"F-score":[84],"60%.":[86],"also":[88],"show":[89],"that":[90],"cosine":[91],"similarity":[92],"features":[93],"are":[94],"more":[95],"effective,":[96],"both":[97],"unsupervised":[99],"systems":[100,106],"(F-score":[101,107],"65%)":[103],"and":[104],"67%).":[109]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}