{"id":"https://openalex.org/W199541590","doi":null,"title":"Corpus Variation and Parser Performance","display_name":"Corpus Variation and Parser Performance","publication_year":2001,"publication_date":"2001-01-01","ids":{"openalex":"https://openalex.org/W199541590","mag":"199541590"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.28.9140","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.28.9140","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.icsi.berkeley.edu/~gildea/gildea-emnlp01.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/A5020945377","display_name":"Daniel Gildea","orcid":"https://orcid.org/0000-0002-7858-2624","observed_orcids":["https://orcid.org/0000-0002-7858-2624"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Daniel Gildea","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-7858-2624","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5020945377"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":11.9612,"has_fulltext":false,"cited_by_count":268,"citation_normalized_percentile":{"value":0.98728929,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":100},"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":0.9994999766349792,"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":0.9994999766349792,"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/T12031","display_name":"Speech and dialogue systems","score":9.999999747378752e-05,"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/T10034","display_name":"Syntax, Semantics, Linguistic Variation","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/1203","display_name":"Language and Linguistics"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/statistical-parsing","display_name":"statistical parsing","score":0.9070000052452087},{"id":"https://openalex.org/keywords/parser-performance","display_name":"parser performance","score":0.6949999928474426},{"id":"https://openalex.org/keywords/model-transportability","display_name":"model portability","score":0.6769999861717224},{"id":"https://openalex.org/keywords/brown-corpus","display_name":"Brown Corpus","score":0.9350000023841858},{"id":"https://openalex.org/keywords/penn-treebank","display_name":"Penn Treebank","score":0.8379999995231628},{"id":"https://openalex.org/keywords/parameter-pruning","display_name":"parameter pruning","score":0.7070000171661377},{"id":"https://openalex.org/keywords/model-size-reduction","display_name":"model size reduction","score":0.46799999475479126}],"concepts":[{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.736522376537323},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.723874032497406},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.5477678775787354},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.46556806564331055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3929665982723236},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0979236364364624},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.06561297178268433}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.28.9140","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.28.9140","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.icsi.berkeley.edu/~gildea/gildea-emnlp01.pdf","raw_type":"text"},{"id":"mag:199541590","doi":null,"is_oa":false,"landing_page_url":"https://www.aclweb.org/anthology/W01-0521.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306418267","display_name":"Empirical Methods in Natural Language Processing","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"Empirical Methods in Natural Language Processing","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1535015163","https://openalex.org/W1567570606","https://openalex.org/W1810157568","https://openalex.org/W1986543644","https://openalex.org/W2092654472","https://openalex.org/W2110882317","https://openalex.org/W2112589528","https://openalex.org/W2121127625","https://openalex.org/W2126414663","https://openalex.org/W2136028896","https://openalex.org/W2138433202","https://openalex.org/W2142708806","https://openalex.org/W2153439141","https://openalex.org/W2166451556","https://openalex.org/W3089319657"],"related_works":["https://openalex.org/W2163568299","https://openalex.org/W2138382875","https://openalex.org/W2125712079","https://openalex.org/W2097606805","https://openalex.org/W2092654472","https://openalex.org/W1986543644","https://openalex.org/W1632114991","https://openalex.org/W1535015163","https://openalex.org/W1567570606","https://openalex.org/W2139621418","https://openalex.org/W108437174","https://openalex.org/W1985754308","https://openalex.org/W2110882317","https://openalex.org/W2087165009","https://openalex.org/W3021452258","https://openalex.org/W2006734720","https://openalex.org/W2147880316","https://openalex.org/W1574901103","https://openalex.org/W2052449326","https://openalex.org/W1551104980"],"abstract_inverted_index":{"Most":[0],"work":[1],"in":[2],"statistical":[3],"parsing":[4,28,49,103],"has":[5,22,31],"focused":[6],"on":[7,83],"a":[8,92],"single":[9],"corpus:":[10],"the":[11,17,34,62,73,81,99,102],"Wall":[12],"Street":[13],"Journal":[14],"portion":[15],"of":[16,27,36,40,72,101],"Penn":[18],"Treebank.":[19],"While":[20],"this":[21],"allowed":[23],"for":[24,61,94],"quantitative":[25],"comparison":[26],"techniques,":[29],"it":[30,85],"left":[32],"open":[33],"question":[35],"how":[37,47],"other":[38],"types":[39],"text":[41],"might":[42],"a#ect":[43],"parser":[44],"performance,":[45],"and":[46,64,67],"portable":[48],"models":[50],"are":[51,77],"across":[52],"corpora.":[53],"We":[54],"examine":[55],"these":[56],"questions":[57],"by":[58],"comparing":[59],"results":[60],"Brown":[63],"WSJ":[65],"corpora,":[66],"also":[68],"consider":[69],"which":[70,84],"parts":[71],"parser's":[74],"probability":[75],"model":[76],"particularly":[78],"tuned":[79],"to":[80,91,97],"corpus":[82],"was":[86],"trained.":[87],"This":[88],"leads":[89],"us":[90],"technique":[93],"pruning":[95],"parameters":[96],"reduce":[98],"size":[100],"model.":[104],"1":[105]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":8},{"year":2017,"cited_by_count":7},{"year":2016,"cited_by_count":13},{"year":2015,"cited_by_count":11},{"year":2014,"cited_by_count":9},{"year":2013,"cited_by_count":16},{"year":2012,"cited_by_count":19}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2025-10-10T00:00:00"}