{"id":"https://openalex.org/W4412673509","doi":"https://doi.org/10.1145/3731120.3744609","title":"Exploring the Utility of Embedding Similarity for Contract Tasks","display_name":"Exploring the Utility of Embedding Similarity for Contract Tasks","publication_year":2025,"publication_date":"2025-07-18","ids":{"openalex":"https://openalex.org/W4412673509","doi":"https://doi.org/10.1145/3731120.3744609"},"language":"en","primary_location":{"id":"doi:10.1145/3731120.3744609","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3731120.3744609","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval (ICTIR)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043048628","display_name":"Jonathan Donnelly","orcid":null},"institutions":[{"id":"https://openalex.org/I4210093272","display_name":"Nuvo Pharmaceuticals (Canada)","ror":"https://ror.org/00qe6gb33","country_code":"CA","type":"company","lineage":["https://openalex.org/I4210093272"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jonathan Donnelly","raw_affiliation_strings":["Zuva Inc, Toronto, Canada"],"raw_orcid":"https://orcid.org/0009-0002-2938-328X","affiliations":[{"raw_affiliation_string":"Zuva Inc, Toronto, Canada","institution_ids":["https://openalex.org/I4210093272"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048499958","display_name":"Adam Roegiest","orcid":"https://orcid.org/0000-0003-1265-8881"},"institutions":[{"id":"https://openalex.org/I4210093272","display_name":"Nuvo Pharmaceuticals (Canada)","ror":"https://ror.org/00qe6gb33","country_code":"CA","type":"company","lineage":["https://openalex.org/I4210093272"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Adam Roegiest","raw_affiliation_strings":["Zuva Inc, Toronto, Canada"],"raw_orcid":"https://orcid.org/0000-0003-1265-8881","affiliations":[{"raw_affiliation_string":"Zuva Inc, Toronto, Canada","institution_ids":["https://openalex.org/I4210093272"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210093272"],"apc_list":null,"apc_paid":null,"fwci":1.673,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.83030974,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"401","last_page":"409"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10456","display_name":"Multi-Agent Systems and Negotiation","score":0.9962000250816345,"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/T10456","display_name":"Multi-Agent Systems and Negotiation","score":0.9962000250816345,"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":0.9911999702453613,"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/T13643","display_name":"Artificial Intelligence in Law","score":0.9824000000953674,"subfield":{"id":"https://openalex.org/subfields/3320","display_name":"Political Science and International Relations"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.7164389491081238},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.7127580642700195},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6165379285812378},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39189857244491577},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.05808982253074646}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7164389491081238},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.7127580642700195},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6165379285812378},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39189857244491577},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.05808982253074646}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3731120.3744609","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3731120.3744609","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval (ICTIR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1835419070","https://openalex.org/W1880262756","https://openalex.org/W1972208442","https://openalex.org/W1996936615","https://openalex.org/W1997632490","https://openalex.org/W2069270778","https://openalex.org/W2077501222","https://openalex.org/W2085922539","https://openalex.org/W2149427297","https://openalex.org/W2751698296","https://openalex.org/W2955281392","https://openalex.org/W3021397474","https://openalex.org/W3099950029","https://openalex.org/W3209981429","https://openalex.org/W4233145949","https://openalex.org/W4387846582","https://openalex.org/W4389519598","https://openalex.org/W4401330297","https://openalex.org/W4408141654","https://openalex.org/W4411630018"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2081900870","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890"],"abstract_inverted_index":{"With":[0],"the":[1,9,23,31,125],"increasing":[2],"use":[3],"of":[4,11,26,34,64,110,164],"text":[5,27],"embeddings":[6,28,49],"motivated":[7],"by":[8],"adoption":[10],"Retrieval":[12],"Augmented":[13],"Generation":[14],"(RAG)":[15],"in":[16,37,141],"applied":[17],"domains,":[18],"this":[19],"work":[20,151],"investigates":[21],"whether":[22],"semantic":[24,35,138],"aspects":[25],"correspond":[29],"to":[30,74,155],"colloquial":[32],"understanding":[33,63],"similarity":[36,52,93,139],"a":[38,61,75,114,120,146],"legal":[39,44,62,69,84],"domain.":[40],"Using":[41],"clauses":[42,70],"from":[43,148],"agreements,":[45],"we":[46],"find":[47],"that":[48,97,130],"and":[50,92,144],"associated":[51],"measurements":[53],"(e.g.,":[54],"cosine,":[55],"L2)":[56],"do":[57],"not":[58],"accurately":[59],"reflect":[60],"''semantically":[65],"similar.''":[66],"More":[67],"specifically,":[68],"are":[71],"more":[72],"similar":[73],"minimally":[76],"changed,":[77],"negated":[78],"version":[79],"than":[80],"those":[81],"with":[82,106],"identical":[83],"meaning":[85],"but":[86],"worded":[87],"differently":[88],"across":[89],"embedding":[90],"sources":[91],"measures.":[94],"We":[95],"demonstrate":[96],"discriminative":[98],"classification":[99],"can":[100,152],"be":[101,133,153],"an":[102],"effective":[103,158],"stop-gap":[104],"solution":[105],"these":[107],"two":[108],"types":[109],"variants":[111],"using":[112],"either":[113],"zero-shot":[115],"generative":[116],"model":[117],"prompt":[118],"or":[119],"multi-layer":[121],"perceptron":[122],"trained":[123],"on":[124],"embeddings.":[126],"These":[127],"results":[128],"indicate":[129],"care":[131],"should":[132],"taken":[134],"when":[135],"applying":[136],"off-the-shelf":[137],"tools":[140],"specialized":[142],"domains":[143],"provides":[145],"basis":[147],"which":[149],"further":[150],"conducted":[154],"determine":[156],"cost":[157],"methods":[159],"for":[160],"measuring":[161],"nuanced":[162],"notions":[163],"similarity.":[165]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
