{"id":"https://openalex.org/W2963463240","doi":"https://doi.org/10.18653/v1/k17-1040","title":"Neural Structural Correspondence Learning for Domain Adaptation","display_name":"Neural Structural Correspondence Learning for Domain Adaptation","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2963463240","doi":"https://doi.org/10.18653/v1/k17-1040","mag":"2963463240"},"language":"en","primary_location":{"id":"doi:10.18653/v1/k17-1040","doi":"https://doi.org/10.18653/v1/k17-1040","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/k17-1040","pdf_url":"https://www.aclweb.org/anthology/K17-1040.pdf","source":{"id":"https://openalex.org/S4306418031","display_name":"Conference on Computational Natural Language Learning","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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 21st Conference on Computational Natural Language Learning (CoNLL 2017)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://www.aclweb.org/anthology/K17-1040.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003744048","display_name":"Yftah Ziser","orcid":"https://orcid.org/0009-0002-6228-9471","observed_orcids":["https://orcid.org/0009-0002-6228-9471"]},"institutions":[{"id":"https://openalex.org/I174306211","display_name":"Technion \u2013 Israel Institute of Technology","ror":"https://ror.org/03qryx823","country_code":"IL","type":"education","lineage":["https://openalex.org/I174306211"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Yftah Ziser","raw_affiliation_strings":["Faculty of Industrial Engineering and Management, Technion, IIT"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Industrial Engineering and Management, Technion, IIT","institution_ids":["https://openalex.org/I174306211"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054952724","display_name":"Roi Reichart","orcid":"https://orcid.org/0000-0001-6918-0554","observed_orcids":["https://orcid.org/0000-0001-6918-0554"]},"institutions":[{"id":"https://openalex.org/I174306211","display_name":"Technion \u2013 Israel Institute of Technology","ror":"https://ror.org/03qryx823","country_code":"IL","type":"education","lineage":["https://openalex.org/I174306211"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Roi Reichart","raw_affiliation_strings":["Faculty of Industrial Engineering and Management, Technion, IIT"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Industrial Engineering and Management, Technion, IIT","institution_ids":["https://openalex.org/I174306211"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I174306211"],"apc_list":null,"apc_paid":null,"fwci":8.3691,"has_fulltext":true,"cited_by_count":130,"citation_normalized_percentile":{"value":0.98257587,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"400","last_page":"410"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9907000064849854,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9907000064849854,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.007899999618530273,"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":0.0007999999797903001,"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/domain-adaptation","display_name":"domain adaptation","score":0.9700000286102295},{"id":"https://openalex.org/keywords/autoencoder","display_name":"autoencoder","score":0.8450000286102295},{"id":"https://openalex.org/keywords/representation-learning","display_name":"representation learning","score":0.7129999995231628},{"id":"https://openalex.org/keywords/cross-domain-sentiment-classification","display_name":"cross-domain sentiment analysis","score":0.7860000133514404},{"id":"https://openalex.org/keywords/word-embeddings","display_name":"word embeddings","score":0.7739999890327454},{"id":"https://openalex.org/keywords/low-dimensional-representation","display_name":"low-dimensional representation","score":0.5870000123977661},{"id":"https://openalex.org/keywords/neural-network-model","display_name":"neural network model","score":0.32100000977516174}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8315801620483398},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7629562020301819},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7114675045013428},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6970388889312744},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6925429105758667},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.6362758874893188},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6145437955856323},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6086481213569641},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.5894569754600525},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5826155543327332},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5784556269645691},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.49250635504722595},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41286739706993103},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.393459677696228},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3688722848892212},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3520869016647339},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09699729084968567},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/v1/k17-1040","doi":"https://doi.org/10.18653/v1/k17-1040","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/k17-1040","pdf_url":"https://www.aclweb.org/anthology/K17-1040.pdf","source":{"id":"https://openalex.org/S4306418031","display_name":"Conference on Computational Natural Language Learning","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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 21st Conference on Computational Natural Language Learning (CoNLL 2017)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1610.01588","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1610.01588","pdf_url":"https://arxiv.org/pdf/1610.01588","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},{"id":"doi:10.48550/arxiv.1610.01588","doi":"https://doi.org/10.48550/arxiv.1610.01588","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1610.01588","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.18653/v1/k17-1040","doi":"https://doi.org/10.18653/v1/k17-1040","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/k17-1040","pdf_url":"https://www.aclweb.org/anthology/K17-1040.pdf","source":{"id":"https://openalex.org/S4306418031","display_name":"Conference on Computational Natural Language Learning","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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 21st Conference on Computational Natural Language Learning (CoNLL 2017)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2963463240.pdf","grobid_xml":"https://content.openalex.org/works/W2963463240.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W22861983","https://openalex.org/W98423455","https://openalex.org/W181016146","https://openalex.org/W774326453","https://openalex.org/W1496797923","https://openalex.org/W1571969069","https://openalex.org/W1731081199","https://openalex.org/W1882958252","https://openalex.org/W1909320841","https://openalex.org/W1932968309","https://openalex.org/W1956343362","https://openalex.org/W1959608418","https://openalex.org/W2014703436","https://openalex.org/W2025768430","https://openalex.org/W2032235985","https://openalex.org/W2068463433","https://openalex.org/W2096873754","https://openalex.org/W2103471677","https://openalex.org/W2104094955","https://openalex.org/W2105523772","https://openalex.org/W2110798204","https://openalex.org/W2111362445","https://openalex.org/W2112483442","https://openalex.org/W2120354757","https://openalex.org/W2120708938","https://openalex.org/W2123160630","https://openalex.org/W2130903752","https://openalex.org/W2144321756","https://openalex.org/W2145837098","https://openalex.org/W2153353890","https://openalex.org/W2153579005","https://openalex.org/W2158108973","https://openalex.org/W2163302275","https://openalex.org/W2168897002","https://openalex.org/W2215421138","https://openalex.org/W2251732921","https://openalex.org/W2507341862","https://openalex.org/W2567698949","https://openalex.org/W2950133940","https://openalex.org/W2962750142","https://openalex.org/W3146885639"],"related_works":["https://openalex.org/W2803777992","https://openalex.org/W1970476059","https://openalex.org/W3085822291","https://openalex.org/W2158108973","https://openalex.org/W1764158405","https://openalex.org/W113837456","https://openalex.org/W3083515246","https://openalex.org/W2567698949","https://openalex.org/W2962755455","https://openalex.org/W1904385746"],"abstract_inverted_index":{"We":[0],"introduce":[1],"a":[2,37,52,92],"neural":[3,32],"network":[4],"model":[5,35,108],"that":[6,40,56,63,84],"marries":[7],"together":[8],"ideas":[9],"from":[10,83],"two":[11],"prominent":[12,65],"strands":[13],"of":[14,47,59,124],"research":[15],"on":[16,128],"domain":[17,130],"adaptation":[18],"through":[19],"representation":[20,87],"learning:":[21],"structural":[22],"correspondence":[23],"learning":[24,93],"(SCL,":[25],"(Blitzer":[26],"et":[27],"al.,":[28],"2006))":[29],"and":[30,69,132],"autoencoder":[31],"networks":[33],"(NNs).Our":[34],"is":[36,88],"three-layer":[38],"NN":[39],"learns":[41],"to":[42,101,111],"encode":[43],"the":[44,57,74,78,96,122],"non-pivot":[45],"features":[46,61],"an":[48],"input":[49],"example":[50,79],"into":[51,106],"lowdimensional":[53],"representation,":[54],"so":[55],"existence":[58],"pivot":[60,118],"(features":[62],"are":[64],"in":[66,77,91,109],"both":[67],"domains":[68],"convey":[70],"useful":[71],"information":[72],"for":[73,95],"NLP":[75],"task)":[76],"can":[80],"be":[81],"decoded":[82],"representation.The":[85],"low-dimensional":[86],"then":[89],"employed":[90],"algorithm":[94],"task.Moreover,":[97],"we":[98],"show":[99,133],"how":[100],"inject":[102],"pre-trained":[103],"word":[104],"embeddings":[105],"our":[107],"order":[110],"improve":[112],"generalization":[113],"across":[114],"examples":[115],"with":[116,121],"similar":[117],"features.We":[119],"experiment":[120],"task":[123],"cross-domain":[125],"sentiment":[126],"classification":[127],"16":[129],"pairs":[131],"substantial":[134],"improvements":[135],"over":[136],"strong":[137],"baselines.1":[138]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":20},{"year":2020,"cited_by_count":48},{"year":2019,"cited_by_count":15},{"year":2018,"cited_by_count":10},{"year":2017,"cited_by_count":3},{"year":2015,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2019-07-30T00:00:00"}