{"id":"https://openalex.org/W2127202248","doi":"https://doi.org/10.3115/v1/d14-1178","title":"Random Manhattan Integer Indexing: Incremental L1 Normed Vector Space Construction","display_name":"Random Manhattan Integer Indexing: Incremental L1 Normed Vector Space Construction","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W2127202248","doi":"https://doi.org/10.3115/v1/d14-1178","mag":"2127202248"},"language":"en","primary_location":{"id":"doi:10.3115/v1/d14-1178","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/d14-1178","pdf_url":null,"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 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3115/v1/d14-1178","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015110627","display_name":"Behrang QasemiZadeh","orcid":"https://orcid.org/0000-0001-5720-0845"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Behrang Q. Zadeh","raw_affiliation_strings":["The Insight Centre for Data Analytics"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Insight Centre for Data Analytics","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024322708","display_name":"Siegfried Handschuh","orcid":"https://orcid.org/0000-0002-6195-9034"},"institutions":[{"id":"https://openalex.org/I186354981","display_name":"University of Passau","ror":"https://ror.org/05ydjnb78","country_code":"DE","type":"education","lineage":["https://openalex.org/I186354981"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Siegfried Handschuh","raw_affiliation_strings":["University Of Passau#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University Of Passau#TAB#","institution_ids":["https://openalex.org/I186354981"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.4851,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.91134286,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1713","last_page":"1723"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.996999979019165,"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":0.996999979019165,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9962999820709229,"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/T11269","display_name":"Algorithms and Data Compression","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"}}],"keywords":[{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.6881725788116455},{"id":"https://openalex.org/keywords/search-engine-indexing","display_name":"Search engine indexing","score":0.6871556043624878},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.6863016486167908},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5936647057533264},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5312561392784119},{"id":"https://openalex.org/keywords/integer","display_name":"Integer (computer science)","score":0.5299274325370789},{"id":"https://openalex.org/keywords/vector-space","display_name":"Vector space","score":0.46992433071136475},{"id":"https://openalex.org/keywords/normed-vector-space","display_name":"Normed vector space","score":0.46108195185661316},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4202558994293213},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.419921338558197},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4162311255931854},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.35461682081222534},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.33483368158340454},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.30310916900634766},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.26296013593673706},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.19416630268096924},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.10106927156448364}],"concepts":[{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.6881725788116455},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.6871556043624878},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.6863016486167908},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5936647057533264},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5312561392784119},{"id":"https://openalex.org/C97137487","wikidata":"https://www.wikidata.org/wiki/Q729138","display_name":"Integer (computer science)","level":2,"score":0.5299274325370789},{"id":"https://openalex.org/C13336665","wikidata":"https://www.wikidata.org/wiki/Q125977","display_name":"Vector space","level":2,"score":0.46992433071136475},{"id":"https://openalex.org/C171739935","wikidata":"https://www.wikidata.org/wiki/Q726210","display_name":"Normed vector space","level":2,"score":0.46108195185661316},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4202558994293213},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.419921338558197},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4162311255931854},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.35461682081222534},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33483368158340454},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30310916900634766},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.26296013593673706},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.19416630268096924},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.10106927156448364},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3115/v1/d14-1178","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/d14-1178","pdf_url":null,"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 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","raw_type":"proceedings-article"},{"id":"pmh:oai:https://researchrepository.universityofgalway.ie:10379/4650","is_oa":true,"landing_page_url":"http://hdl.handle.net/10379/4650","pdf_url":"http://hdl.handle.net/10379/4650","source":{"id":"https://openalex.org/S4377196322","display_name":"ARAN (University of Galway Research Repository) (Ollscoil na Gaillimhe \u2013 University of Galway)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I188760350","host_organization_name":"Ollscoil na Gaillimhe \u2013 University of Galway","host_organization_lineage":["https://openalex.org/I188760350"],"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":"conference paper"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.671.1722","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.671.1722","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://emnlp2014.org/papers/pdf/EMNLP2014178.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.697.6785","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.697.6785","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://aclweb.org/anthology/D/D14/D14-1178.pdf","raw_type":"text"},{"id":"doi:10.13025/21180","is_oa":true,"landing_page_url":"https://doi.org/10.13025/21180","pdf_url":null,"source":{"id":"https://openalex.org/S4306402280","display_name":"Research Repository UCD (University College Dublin)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I100930933","host_organization_name":"University College Dublin","host_organization_lineage":["https://openalex.org/I100930933"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Other"}],"best_oa_location":{"id":"doi:10.3115/v1/d14-1178","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/d14-1178","pdf_url":null,"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 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W188912188","https://openalex.org/W193898724","https://openalex.org/W1525999779","https://openalex.org/W1551441240","https://openalex.org/W1584739173","https://openalex.org/W1662133657","https://openalex.org/W1978400666","https://openalex.org/W1992633833","https://openalex.org/W2014689751","https://openalex.org/W2044466562","https://openalex.org/W2045533739","https://openalex.org/W2053171205","https://openalex.org/W2120084270","https://openalex.org/W2147152072","https://openalex.org/W2149463264","https://openalex.org/W2154249783","https://openalex.org/W2155870214","https://openalex.org/W2169240343","https://openalex.org/W2622496757","https://openalex.org/W2979473749","https://openalex.org/W3097609957"],"related_works":["https://openalex.org/W4245268118","https://openalex.org/W4382600406","https://openalex.org/W2617085105","https://openalex.org/W4231104074","https://openalex.org/W2374594895","https://openalex.org/W2620361398","https://openalex.org/W4210663730","https://openalex.org/W115431596","https://openalex.org/W325613082","https://openalex.org/W2017525113"],"abstract_inverted_index":{"Vector":[0],"space":[1,163],"models":[2],"(VSMs)":[3],"are":[4],"mathematically":[5],"well-defined":[6],"frameworks":[7],"that":[8,37,60],"have":[9],"been":[10],"widely":[11],"used":[12,151],"in":[13,142,160],"the":[14,29,35,56,86,97,121,143,155],"distributional":[15],"approaches":[16],"to":[17,55,70,115,153],"semantics.":[18],"In":[19,26,113],"VSMs,":[20],"high-dimensional":[21],"vectors":[22,32,159],"represent":[23,39],"linguistic":[24],"entities.":[25],"an":[27,106],"application,":[28],"similarity":[30],"of":[31,49,58,88,99,138,164],"and":[33,102,108,147],"thus":[34,109],"entities":[36],"they":[38],"is":[40,52,68],"computed":[41],"by":[42],"a":[43,53,64,77,100,134,161],"distance":[44],"formula.":[45],"The":[46],"high":[47],"dimensionality":[48,65],"vectors,":[50],"however,":[51],"barrier":[54],"performance":[57],"methods":[59],"employ":[61],"VSMs.":[62],"Consequently,":[63],"reduction":[66,104],"technique":[67,79],"employed":[69],"alleviate":[71],"this":[72],"problem.":[73],"This":[74],"paper":[75],"introduces":[76],"novel":[78],"called":[80],"Random":[81,129],"Manhattan":[82,130],"Indexing":[83,132],"(RMI)":[84],"for":[85],"construction":[87,98],"L1":[89,156],"normed":[90],"VSMs":[91],"at":[92],"reduced":[93],"dimensionality.":[94,166],"RMI":[95,119,146],"combines":[96],"VSM":[101],"dimension":[103],"into":[105],"incremental":[107],"scalable":[110],"two-step":[111],"procedure.":[112],"order":[114],"attain":[116],"its":[117],"goal,":[118],"employs":[120],"sparse":[122],"Cauchy":[123],"random":[124],"projections.":[125],"We":[126],"further":[127],"introduce":[128],"Integer":[131],"(RMII):":[133],"computationally":[135],"enhanced":[136],"version":[137],"RMI.":[139],"As":[140],"shown":[141],"reported":[144],"experiments,":[145],"RMII":[148],"can":[149],"be":[150],"reliably":[152],"estimate":[154],"distances":[157],"between":[158],"vector":[162],"low":[165]},"counts_by_year":[{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2026-07-02T09:51:11.867554","created_date":"2025-10-10T00:00:00"}