{"id":"https://openalex.org/W2064111571","doi":"https://doi.org/10.1145/2766462.2767805","title":"Combining Orthogonal Information in Large-Scale Cross-Language Information Retrieval","display_name":"Combining Orthogonal Information in Large-Scale Cross-Language Information Retrieval","publication_year":2015,"publication_date":"2015-08-04","ids":{"openalex":"https://openalex.org/W2064111571","doi":"https://doi.org/10.1145/2766462.2767805","mag":"2064111571"},"language":"en","primary_location":{"id":"doi:10.1145/2766462.2767805","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2766462.2767805","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"article","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/A5068853519","display_name":"Shigehiko Schamoni","orcid":"https://orcid.org/0000-0002-0304-2412"},"institutions":[{"id":"https://openalex.org/I223822909","display_name":"Heidelberg University","ror":"https://ror.org/038t36y30","country_code":"DE","type":"education","lineage":["https://openalex.org/I223822909"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Shigehiko Schamoni","raw_affiliation_strings":["Heidelberg University, Heidelberg, Germany"],"affiliations":[{"raw_affiliation_string":"Heidelberg University, Heidelberg, Germany","institution_ids":["https://openalex.org/I223822909"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011749347","display_name":"Stefan Riezler","orcid":null},"institutions":[{"id":"https://openalex.org/I223822909","display_name":"Heidelberg University","ror":"https://ror.org/038t36y30","country_code":"DE","type":"education","lineage":["https://openalex.org/I223822909"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Stefan Riezler","raw_affiliation_strings":["Heidelberg University, Heidelberg, Germany"],"affiliations":[{"raw_affiliation_string":"Heidelberg University, Heidelberg, Germany","institution_ids":["https://openalex.org/I223822909"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5068853519"],"corresponding_institution_ids":["https://openalex.org/I223822909"],"apc_list":null,"apc_paid":null,"fwci":0.8897,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.82832399,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"943","last_page":"946"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9983999729156494,"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":0.9983999729156494,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9983000159263611,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.8600804805755615},{"id":"https://openalex.org/keywords/orthogonality","display_name":"Orthogonality","score":0.7304413914680481},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.680699348449707},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5506805181503296},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.547569215297699},{"id":"https://openalex.org/keywords/cross-language-information-retrieval","display_name":"Cross-language information retrieval","score":0.5438566207885742},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5405811667442322},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.526301383972168},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.5125613212585449},{"id":"https://openalex.org/keywords/machine-translation","display_name":"Machine translation","score":0.4918537139892578},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.487839013338089},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.29240429401397705},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0758277177810669}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8600804805755615},{"id":"https://openalex.org/C17137986","wikidata":"https://www.wikidata.org/wiki/Q215067","display_name":"Orthogonality","level":2,"score":0.7304413914680481},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.680699348449707},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5506805181503296},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.547569215297699},{"id":"https://openalex.org/C2778842860","wikidata":"https://www.wikidata.org/wiki/Q986551","display_name":"Cross-language information retrieval","level":3,"score":0.5438566207885742},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5405811667442322},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.526301383972168},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.5125613212585449},{"id":"https://openalex.org/C203005215","wikidata":"https://www.wikidata.org/wiki/Q79798","display_name":"Machine translation","level":2,"score":0.4918537139892578},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.487839013338089},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.29240429401397705},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0758277177810669},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2766462.2767805","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2766462.2767805","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.724.7446","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.724.7446","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cl.uni-heidelberg.de/%7Eriezler/publications/papers/SIGIR2015.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8100000023841858,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W143382514","https://openalex.org/W336359994","https://openalex.org/W1500227852","https://openalex.org/W1973435495","https://openalex.org/W1988686126","https://openalex.org/W1990190154","https://openalex.org/W2009077327","https://openalex.org/W2011548984","https://openalex.org/W2011756549","https://openalex.org/W2025423507","https://openalex.org/W2069870183","https://openalex.org/W2084457609","https://openalex.org/W2120165387","https://openalex.org/W2142074148","https://openalex.org/W2155607551","https://openalex.org/W2204449939","https://openalex.org/W2250400351","https://openalex.org/W2250473075","https://openalex.org/W3021520429","https://openalex.org/W6677731365"],"related_works":["https://openalex.org/W2095582735","https://openalex.org/W2059318893","https://openalex.org/W1965698851","https://openalex.org/W834942123","https://openalex.org/W4232542516","https://openalex.org/W1967331680","https://openalex.org/W3176637561","https://openalex.org/W1587967017","https://openalex.org/W130629949","https://openalex.org/W2741140057"],"abstract_inverted_index":{"System":[0],"combination":[1,99],"is":[2],"an":[3,62],"effective":[4],"strategy":[5],"to":[6,28,38,47],"boost":[7],"retrieval":[8,18,26,119],"performance,":[9],"especially":[10],"in":[11,86],"complex":[12],"applications":[13],"such":[14],"as":[15],"cross-language":[16],"information":[17],"(CLIR)":[19],"where":[20],"the":[21,92,101,105,113,116],"aspects":[22],"of":[23,44,58,82,100],"translation":[24],"and":[25,60,71],"have":[27],"be":[29,89],"optimized":[30],"jointly.":[31],"We":[32,51,78],"focus":[33],"on":[34,65],"machine":[35],"learning-based":[36],"approaches":[37],"CLIR":[39],"that":[40,80,103],"need":[41],"large":[42],"sets":[43],"relevance-ranked":[45],"data":[46],"train":[48],"high-dimensional":[49],"models.":[50],"compare":[52],"these":[53],"models":[54,102,114],"under":[55],"various":[56],"measures":[57],"orthogonality,":[59],"present":[61],"experimental":[63],"evaluation":[64],"two":[66,72],"different":[67,73],"domains":[68],"(patents,":[69],"Wikipedia)":[70],"language":[74],"pairs":[75],"(Japanese-English,":[76],"German-English).":[77],"show":[79],"gains":[81],"over":[83,91],"10":[84],"points":[85],"MAP/NDCG":[87],"can":[88],"achieved":[90],"best":[93,117],"single":[94],"model":[95],"by":[96,111],"a":[97],"linear":[98],"contribute":[104],"most":[106],"orthogonal":[107],"information,":[108],"rather":[109],"than":[110],"combining":[112],"with":[115],"standalone":[118],"performance.":[120]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}