{"id":"https://openalex.org/W11313625","doi":null,"title":"Robust, Light-weight Approaches to compute Lexical Similarity","display_name":"Robust, Light-weight Approaches to compute Lexical Similarity","publication_year":2010,"publication_date":"2010-05-06","ids":{"openalex":"https://openalex.org/W11313625","mag":"11313625"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.453.6947","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.453.6947","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://cogcomp.cs.illinois.edu/papers/similarityMetrics.pdf","raw_type":"text"},"volume":null,"type":"article","indexed_in":[],"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/A5112564190","display_name":"Quang Do","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Quang Do","raw_affiliation_strings":["University of Illinois at Urbana Champaign"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023802054","display_name":"Dan Roth","orcid":"https://orcid.org/0009-0002-1447-5173","observed_orcids":["https://orcid.org/0009-0002-1447-5173"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dan Roth","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0009-0002-1447-5173","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081308684","display_name":"Mark Sammons","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mark Sammons","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070195575","display_name":"Yuancheng Tu","orcid":"https://orcid.org/0000-0003-0875-7625","observed_orcids":["https://orcid.org/0000-0003-0875-7625"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuancheng Tu","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-0875-7625","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5050525772","display_name":"V. G. Vinod Vydiswaran","orcid":"https://orcid.org/0000-0002-3122-1936","observed_orcids":["https://orcid.org/0000-0002-3122-1936"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"V. G. Vinod Vydiswaran","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-3122-1936","affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.4212,"has_fulltext":false,"cited_by_count":41,"citation_normalized_percentile":{"value":0.96331973,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.6011000275611877,"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":0.6011000275611877,"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":0.38350000977516174,"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/T11719","display_name":"Data Quality and Management","score":0.007899999618530273,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/word-similarity","display_name":"word similarity","score":0.9959999918937683},{"id":"https://openalex.org/keywords/similarity-measures","display_name":"similarity measures","score":0.6100000143051147},{"id":"https://openalex.org/keywords/noise-robustness","display_name":"noise robustness","score":0.6700000166893005}],"concepts":[{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.7958093881607056},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7051923274993896},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6686279773712158},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6364195346832275},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.6150713562965393},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5885600447654724},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.47881990671157837},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.46168747544288635},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.45744583010673523},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3571520745754242},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18325069546699524},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07883128523826599},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.453.6947","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.453.6947","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://cogcomp.cs.illinois.edu/papers/similarityMetrics.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.648.5170","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.648.5170","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://cogcomp.cs.illinois.edu/~quangdo2/papers/DRSTV09.pdf","raw_type":"text"},{"id":"pmh:oai:www.ideals.illinois.edu:2142/15462","doi":null,"is_oa":false,"landing_page_url":"http://hdl.handle.net/2142/15462","pdf_url":null,"source":{"id":"https://openalex.org/S4306401358","display_name":"Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801919071","host_organization_name":"University of Illinois System","host_organization_lineage":["https://openalex.org/I2801919071"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Technical Report"},{"id":"mag:11313625","doi":null,"is_oa":false,"landing_page_url":"http://cogcomp.cs.illinois.edu/papers/DRSTV09.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1567833515","https://openalex.org/W1592081640","https://openalex.org/W1646278814","https://openalex.org/W1980776243","https://openalex.org/W2039176871","https://openalex.org/W2132019450","https://openalex.org/W2142120379","https://openalex.org/W2525127255"],"related_works":["https://openalex.org/W2772856098","https://openalex.org/W1996356482","https://openalex.org/W1990524510","https://openalex.org/W2251797829","https://openalex.org/W6903458332","https://openalex.org/W2250888215","https://openalex.org/W7099324843","https://openalex.org/W7099575244","https://openalex.org/W7096837116","https://openalex.org/W2901943172"],"abstract_inverted_index":{"Most":[0],"text":[1],"processing":[2,21],"systems":[3],"need":[4],"to":[5,55,75,88,123,129,139,151,156],"compare":[6],"lexical":[7],"units":[8],"\u2013":[9,14],"words,":[10],"entities,":[11],"semantic":[12],"concepts":[13],"with":[15],"each":[16],"other":[17],"as":[18],"a":[19,117,143],"basic":[20],"step":[22],"within":[23],"large":[24],"and":[25,38,95,153],"complex":[26],"systems.":[27],"A":[28],"significant":[29],"amount":[30],"of":[31,66,81,145],"research":[32],"has":[33],"taken":[34],"place":[35],"in":[36,70,102,142],"formu-lating":[37],"evaluating":[39],"multiple":[40],"similar-ity":[41,77],"metrics,":[42],"primarily":[43],"between":[44,78],"words.":[45],"Often,":[46],"such":[47,125],"techniques":[48],"are":[49,52],"resource-intensive":[50],"or":[51],"applicable":[53],"only":[54],"spe-cific":[56],"use":[57],"cases.":[58],"In":[59],"this":[60],"technical":[61],"re-port,":[62],"we":[63],"summarize":[64],"some":[65],"our":[67,103],"re-search":[68],"work":[69],"finding":[71],"robust,":[72],"light-weight":[73],"approaches":[74],"compute":[76,130],"two":[79,85],"spans":[80],"text.":[82],"We":[83,114,135],"describe":[84],"new":[86],"measures":[87,128],"com-pute":[89],"similarity,":[90],"WNSim":[91],"for":[92,97],"word":[93],"similar-ity,":[94],"NESim":[96],"named":[98],"entity":[99],"sim-ilarity,":[100],"which":[101],"experience":[104],"have":[105,136],"been":[106],"more":[107,110],"useful":[108,141],"than":[109],"standard":[111],"similarity":[112,127,133],"metrics.":[113],"also":[115],"present":[116],"technique,":[118],"Lexical":[119],"Level":[120],"Matching":[121],"(LLM),":[122],"combine":[124],"token-level":[126],"phrase-and":[131],"sentence-level":[132],"scores.":[134],"found":[137],"LLM":[138],"be":[140],"number":[144],"NLP":[146],"applications;":[147],"it":[148],"is":[149],"easy":[150],"compute,":[152],"surprisingly":[154],"robust":[155],"noise.":[157],"1":[158]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":9},{"year":2012,"cited_by_count":6}],"updated_date":"2026-11-01T11:40:01.658920","created_date":"2016-06-24T00:00:00"}
