{"id":"https://openalex.org/W2041522124","doi":"https://doi.org/10.1145/1076034.1076091","title":"Learning to extract information from semi-structured text using a discriminative context free grammar","display_name":"Learning to extract information from semi-structured text using a discriminative context free grammar","publication_year":2005,"publication_date":"2005-08-15","ids":{"openalex":"https://openalex.org/W2041522124","doi":"https://doi.org/10.1145/1076034.1076091","mag":"2041522124"},"language":"en","primary_location":{"id":"doi:10.1145/1076034.1076091","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1076034.1076091","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th annual 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/A5042310818","display_name":"Paul Viola","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Paul Viola","raw_affiliation_strings":["Microsoft Research, Redmond, WA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, Redmond, WA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001944148","display_name":"Mukund Narasimhan","orcid":"https://orcid.org/0009-0003-6489-105X"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mukund Narasimhan","raw_affiliation_strings":["University of Washington, Seattle, WA","University Of Washington (Seattle, WA)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Washington, Seattle, WA","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"University Of Washington (Seattle, WA)","institution_ids":["https://openalex.org/I201448701"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":11.4858,"has_fulltext":false,"cited_by_count":75,"citation_normalized_percentile":{"value":0.98391069,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"330","last_page":"337"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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/T11719","display_name":"Data Quality and Management","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8809366822242737},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.7259291410446167},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7246303558349609},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6981692910194397},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.597294807434082},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.5623844861984253},{"id":"https://openalex.org/keywords/grammar","display_name":"Grammar","score":0.5394957661628723},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4361172914505005},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4280484914779663},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.42493414878845215},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39254990220069885},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.10287502408027649}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8809366822242737},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.7259291410446167},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7246303558349609},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6981692910194397},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.597294807434082},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.5623844861984253},{"id":"https://openalex.org/C26022165","wikidata":"https://www.wikidata.org/wiki/Q8091","display_name":"Grammar","level":2,"score":0.5394957661628723},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4361172914505005},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4280484914779663},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.42493414878845215},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39254990220069885},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.10287502408027649},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1076034.1076091","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1076034.1076091","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7200000286102295}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W147273232","https://openalex.org/W150801922","https://openalex.org/W202303397","https://openalex.org/W1574901103","https://openalex.org/W1580375566","https://openalex.org/W1945700406","https://openalex.org/W1979711143","https://openalex.org/W2008652694","https://openalex.org/W2034797903","https://openalex.org/W2093647425","https://openalex.org/W2112076978","https://openalex.org/W2119821739","https://openalex.org/W2125838338","https://openalex.org/W2139193890","https://openalex.org/W2141099517","https://openalex.org/W2147880316","https://openalex.org/W2154316249","https://openalex.org/W2156515921","https://openalex.org/W2163844356","https://openalex.org/W4249572517","https://openalex.org/W6676769703"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W2356597680","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2163278254","https://openalex.org/W1574213390","https://openalex.org/W2045514505"],"abstract_inverted_index":{"In":[0,215],"recent":[1],"work,":[2],"conditional":[3],"Markov":[4],"chain":[5],"models":[6],"(CMM)":[7],"have":[8,256],"been":[9],"used":[10,105,113,147],"to":[11,36,65,106,209,229,247,262],"extract":[12],"information":[13,135,166],"from":[14,27,89,167],"semi-structured":[15],"text":[16],"(one":[17],"example":[18],"is":[19,112,159],"the":[20,29,38,138,203,210,265],"Conditional":[21],"Random":[22],"Field":[23],"[10]).":[24],"Applications":[25],"range":[26],"finding":[28,37],"author":[30],"and":[31,41,116,173,252],"title":[32],"in":[33,44,133,137,192,196,213],"research":[34],"papers":[35],"phone":[39],"number":[40],"street":[42],"address":[43],"a":[45,52,59,84,117,141,187,193,222,235,250],"web":[46],"page.":[47],"The":[48,92,127,154],"CMM":[49],"framework":[50],"combines":[51],"priori":[53],"knowledge":[54],"encoded":[55],"as":[56,171],"features":[57,123],"with":[58],"set":[60,118],"of":[61,119,140,160,205,240,254],"labeled":[62,244,259],"training":[63,90,110],"data":[64,111],"learn":[66],"an":[67],"efficient":[68],"extraction":[69],"process.":[70],"We":[71],"will":[72],"show":[73,185],"that":[74,186],"similar":[75,208],"problems":[76],"can":[77,103,124,226],"be":[78,104,125,146,227],"solved":[79],"more":[80,114,121],"effectively":[81],"by":[82],"learning":[83],"discriminative":[85],"context":[86],"free":[87],"grammar":[88,93,128],"data.":[91],"has":[94,202],"several":[95],"distinct":[96],"advantages:":[97],"long":[98],"range,":[99],"even":[100],"global,":[101],"constraints":[102],"disambiguate":[107],"entity":[108],"labels;":[109],"efficiently;":[115],"new":[120],"powerful":[122],"introduced.":[126],"based":[129],"approach":[130,190],"also":[131,201],"results":[132,191],"semantic":[134],"(encoded":[136],"form":[139],"parse":[142],"tree)":[143],"which":[144],"could":[145],"for":[148,249,264],"IR":[149],"applications":[150],"like":[151],"question":[152],"answering.":[153],"specific":[155],"problem":[156],"we":[157,184],"consider":[158],"extracting":[161],"personal":[162],"contact,":[163],"or":[164],"address,":[165],"unstructured":[168],"sources":[169],"such":[170],"documents":[172],"emails.":[174],"While":[175],"linear-chain":[176],"CMMs":[177],"perform":[178],"reasonably":[179],"well":[180],"on":[181],"this":[182],"task,":[183],"statistical":[188],"parsing":[189],"50%":[194],"reduction":[195],"error":[197],"rate.":[198],"This":[199],"system":[200,211],"advantage":[204],"being":[206],"interactive,":[207],"described":[212],"[9].":[214],"cases":[216],"where":[217],"there":[218],"are":[219,243],"multiple":[220,231],"errors,":[221],"single":[223],"user":[224],"correction":[225],"propagated":[228],"correct":[230],"errors":[232],"automatically.":[233],"Using":[234],"discriminatively":[236],"trained":[237],"grammar,":[238],"93.71%":[239],"all":[241,257],"tokens":[242,258],"correctly":[245,260],"(compared":[246,261],"88.43%":[248],"CMM)":[251],"72.87%":[253],"records":[255],"45.29%":[263],"CMM).":[266]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":4},{"year":2012,"cited_by_count":5}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}