{"id":"https://openalex.org/W2888794009","doi":"https://doi.org/10.18653/v1/d18-1310","title":"Evaluating the Utility of Hand-crafted Features in Sequence Labelling","display_name":"Evaluating the Utility of Hand-crafted Features in Sequence Labelling","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2888794009","doi":"https://doi.org/10.18653/v1/d18-1310","mag":"2888794009"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d18-1310","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1310","pdf_url":"https://www.aclweb.org/anthology/D18-1310.pdf","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 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D18-1310.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5022151811","display_name":"Minghao Wu","orcid":"https://orcid.org/0000-0001-7185-0318"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]},{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["AU","CN"],"is_corresponding":true,"raw_author_name":"Minghao Wu","raw_affiliation_strings":["JD AI Research, Beijing, China","Work carried out at The University of Melbourne"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"JD AI Research, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]},{"raw_affiliation_string":"Work carried out at The University of Melbourne","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100394562","display_name":"Fei Liu","orcid":"https://orcid.org/0000-0001-7141-5105"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Fei Liu","raw_affiliation_strings":["\u2660 The University of Melbourne, Victoria, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"\u2660 The University of Melbourne, Victoria, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078530959","display_name":"Trevor Cohn","orcid":"https://orcid.org/0000-0003-4363-1673"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Trevor Cohn","raw_affiliation_strings":["\u2660 The University of Melbourne, Victoria, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"\u2660 The University of Melbourne, Victoria, Australia","institution_ids":["https://openalex.org/I165779595"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5022151811"],"corresponding_institution_ids":["https://openalex.org/I165779595","https://openalex.org/I4210103986"],"apc_list":null,"apc_paid":null,"fwci":2.0373,"has_fulltext":true,"cited_by_count":29,"citation_normalized_percentile":{"value":0.90730288,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2850","last_page":"2856"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"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":1.0,"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/T10181","display_name":"Natural Language Processing Techniques","score":1.0,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9958999752998352,"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/computer-science","display_name":"Computer science","score":0.8826711773872375},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8679580688476562},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.7085478901863098},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6821979284286499},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6491197943687439},{"id":"https://openalex.org/keywords/sequence-labeling","display_name":"Sequence labeling","score":0.6447392702102661},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.6357040405273438},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.62333744764328},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5586321949958801},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.530084490776062},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5204994082450867},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5184847712516785},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.44968101382255554},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.4432120621204376},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4249430298805237},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4148064851760864},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36818981170654297},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.36629927158355713}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8826711773872375},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8679580688476562},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7085478901863098},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6821979284286499},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6491197943687439},{"id":"https://openalex.org/C35639132","wikidata":"https://www.wikidata.org/wiki/Q7452468","display_name":"Sequence labeling","level":3,"score":0.6447392702102661},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.6357040405273438},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.62333744764328},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5586321949958801},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.530084490776062},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5204994082450867},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5184847712516785},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.44968101382255554},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.4432120621204376},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4249430298805237},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4148064851760864},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36818981170654297},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.36629927158355713},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"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/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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:10.18653/v1/d18-1310","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1310","pdf_url":"https://www.aclweb.org/anthology/D18-1310.pdf","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 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:jupiter.its.unimelb.edu.au:11343/240906","is_oa":true,"landing_page_url":"http://hdl.handle.net/11343/240906","pdf_url":"http://hdl.handle.net/11343/240906","source":{"id":"https://openalex.org/S4377196259","display_name":"Minerva Access (University of Melbourne)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I165779595","host_organization_name":"The University of Melbourne","host_organization_lineage":["https://openalex.org/I165779595"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Conference on Empirical Methods in Natural Language Processing","raw_type":"Conference Paper"}],"best_oa_location":{"id":"doi:10.18653/v1/d18-1310","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1310","pdf_url":"https://www.aclweb.org/anthology/D18-1310.pdf","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 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.7799999713897705,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2888794009.pdf","grobid_xml":"https://content.openalex.org/works/W2888794009.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1614298861","https://openalex.org/W1665214252","https://openalex.org/W1815076433","https://openalex.org/W1940872118","https://openalex.org/W2004763266","https://openalex.org/W2045993505","https://openalex.org/W2056451646","https://openalex.org/W2101609803","https://openalex.org/W2109926133","https://openalex.org/W2130903752","https://openalex.org/W2144578941","https://openalex.org/W2147880316","https://openalex.org/W2153579005","https://openalex.org/W2157331557","https://openalex.org/W2158899491","https://openalex.org/W2170240176","https://openalex.org/W2250539671","https://openalex.org/W2251559320","https://openalex.org/W2296283641","https://openalex.org/W2739894144","https://openalex.org/W2759098435","https://openalex.org/W2952087486","https://openalex.org/W2952230511","https://openalex.org/W2962739339","https://openalex.org/W2962902328","https://openalex.org/W2962932447","https://openalex.org/W2963012544","https://openalex.org/W2963406669","https://openalex.org/W2963625095","https://openalex.org/W2963626623","https://openalex.org/W2963888305","https://openalex.org/W2963913268","https://openalex.org/W2963963145","https://openalex.org/W2964199361","https://openalex.org/W2964266863","https://openalex.org/W4294170691"],"related_works":["https://openalex.org/W2129146436","https://openalex.org/W2032507829","https://openalex.org/W3012125532","https://openalex.org/W2295030615","https://openalex.org/W3047727388","https://openalex.org/W2900788028","https://openalex.org/W2590462354","https://openalex.org/W2990142167","https://openalex.org/W3066338783","https://openalex.org/W2963537918"],"abstract_inverted_index":{"Conventional":[0],"wisdom":[1],"is":[2],"that":[3,66],"hand-crafted":[4],"features":[5,38,69,121],"are":[6],"redundant":[7],"for":[8,35,70,92],"deep":[9],"learning":[10,45],"models,":[11],"as":[12,39,122],"they":[13],"already":[14],"learn":[15],"adequate":[16],"representations":[17],"of":[18,41,58,80,90,103,117],"text":[19],"automatically":[20],"from":[21],"corpora.":[22],"In":[23],"this":[24,28],"work,":[25],"we":[26,64],"test":[27],"claim":[29],"by":[30],"proposing":[31],"a":[32,42,48,81,87,101],"new":[33],"method":[34],"exploiting":[36],"handcrafted":[37],"part":[40],"novel":[43],"hybrid":[44],"approach,":[46],"incorporating":[47],"feature":[49],"auto-encoder":[50],"loss":[51],"component.":[52],"We":[53,85,108],"evaluate":[54],"on":[55],"the":[56,78,93,115,132,142],"task":[57],"named":[59],"entity":[60],"recognition":[61],"(NER),":[62],"where":[63],"show":[65,130],"including":[67,131],"manual":[68],"partof-speech,":[71],"word":[72],"shapes":[73],"and":[74,128],"gazetteers":[75],"can":[76],"improve":[77],"performance":[79],"neural":[82],"CRF":[83],"model.":[84],"obtain":[86],"F":[88],"1":[89],"91.89":[91],"CoNLL-2003":[94],"English":[95],"shared":[96],"task,":[97],"which":[98],"significantly":[99],"outperforms":[100],"collection":[102],"highly":[104],"competitive":[105],"baseline":[106],"models.":[107],"also":[109],"present":[110],"an":[111],"ablation":[112],"study":[113],"showing":[114],"importance":[116],"autoencoding,":[118],"over":[119],"using":[120],"either":[123],"inputs":[124],"or":[125],"outputs":[126],"alone,":[127],"moreover,":[129],"autoencoder":[133],"components":[134],"reduces":[135],"training":[136],"requirements":[137],"to":[138],"60%,":[139],"while":[140],"retaining":[141],"same":[143],"predictive":[144],"accuracy.":[145]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}