{"id":"https://openalex.org/W2167980204","doi":"https://doi.org/10.3115/1073083.1073116","title":"A study on richer syntactic dependencies for structured language modeling","display_name":"A study on richer syntactic dependencies for structured language modeling","publication_year":2001,"publication_date":"2001-01-01","ids":{"openalex":"https://openalex.org/W2167980204","doi":"https://doi.org/10.3115/1073083.1073116","mag":"2167980204"},"language":"en","primary_location":{"id":"doi:10.3115/1073083.1073116","is_oa":true,"landing_page_url":"https://doi.org/10.3115/1073083.1073116","pdf_url":"https://dl.acm.org/doi/pdf/10.3115/1073083.1073116","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.3115/1073083.1073116","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047997552","display_name":"Peng Xu","orcid":"https://orcid.org/0000-0002-7470-9341"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peng Xu","raw_affiliation_strings":["Johns Hopkins University, Baltimore, MD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Johns Hopkins University, Baltimore, MD","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068010225","display_name":"Ciprian Chelba","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":"Ciprian Chelba","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/A5110017489","display_name":"Frederick Jelinek","orcid":null},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Frederick Jelinek","raw_affiliation_strings":["Johns Hopkins University, Baltimore, MD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Johns Hopkins University, Baltimore, MD","institution_ids":["https://openalex.org/I145311948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":47,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"191","last_page":"191"},"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":0.9966999888420105,"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/treebank","display_name":"Treebank","score":0.9167114496231079},{"id":"https://openalex.org/keywords/perplexity","display_name":"Perplexity","score":0.898022472858429},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7848409414291382},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.7817971110343933},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.7018741369247437},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.6870222687721252},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6580961346626282},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5082178115844727},{"id":"https://openalex.org/keywords/recall-rate","display_name":"Recall rate","score":0.49583563208580017},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.48764005303382874},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.4736945629119873},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.47350412607192993},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.380088210105896},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.11119571328163147}],"concepts":[{"id":"https://openalex.org/C206134035","wikidata":"https://www.wikidata.org/wiki/Q811525","display_name":"Treebank","level":3,"score":0.9167114496231079},{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.898022472858429},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7848409414291382},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.7817971110343933},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.7018741369247437},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.6870222687721252},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6580961346626282},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5082178115844727},{"id":"https://openalex.org/C2987098735","wikidata":"https://www.wikidata.org/wiki/Q3808900","display_name":"Recall rate","level":2,"score":0.49583563208580017},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.48764005303382874},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4736945629119873},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.47350412607192993},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.380088210105896},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.11119571328163147},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3115/1073083.1073116","is_oa":true,"landing_page_url":"https://doi.org/10.3115/1073083.1073116","pdf_url":"https://dl.acm.org/doi/pdf/10.3115/1073083.1073116","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.19.9693","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.9693","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://acl.ldc.upenn.edu/P/P02/P02-1025.pdf","raw_type":"text"}],"best_oa_location":{"id":"doi:10.3115/1073083.1073116","is_oa":true,"landing_page_url":"https://doi.org/10.3115/1073083.1073116","pdf_url":"https://dl.acm.org/doi/pdf/10.3115/1073083.1073116","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2167980204.pdf","grobid_xml":"https://content.openalex.org/works/W2167980204.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W1530801890","https://openalex.org/W1535015163","https://openalex.org/W1543082528","https://openalex.org/W1551104980","https://openalex.org/W1632114991","https://openalex.org/W1642730643","https://openalex.org/W1810157568","https://openalex.org/W1989705153","https://openalex.org/W2024490156","https://openalex.org/W2049633694","https://openalex.org/W2092654472","https://openalex.org/W2096175520","https://openalex.org/W2121651659","https://openalex.org/W2155693943","https://openalex.org/W2963847008"],"related_works":["https://openalex.org/W2739034105","https://openalex.org/W4301800915","https://openalex.org/W2963839582","https://openalex.org/W2964047924","https://openalex.org/W2743945814","https://openalex.org/W4299838440","https://openalex.org/W2962832505","https://openalex.org/W2151348424","https://openalex.org/W2153295945","https://openalex.org/W1578024259"],"abstract_inverted_index":{"We":[0,30],"study":[1],"the":[2,9,12,44,49,52,69,72,79,91,95],"impact":[3],"of":[4,11,63,71,101],"richer":[5],"syntactic":[6],"dependencies":[7],"on":[8,51],"performance":[10,65,82],"structured":[13],"language":[14,80,115],"model":[15,81,98],"(SLM)":[16],"along":[17],"three":[18],"dimensions:":[19],"parsing":[20,64],"accuracy":[21],"(LP/LR),":[22],"perplexity":[23],"(PPL)":[24],"and":[25,55,78,84],"word-error-rate":[26],"(WER,":[27],"N-best":[28],"re-scoring).":[29],"show":[31],"that":[32,90],"our":[33],"models":[34],"achieve":[35],"an":[36],"improvement":[37],"in":[38,99,107],"LP/LR,":[39],"PPL":[40],"and/or":[41],"WER":[42,102],"over":[43],"reported":[45],"baseline":[46,96],"results":[47],"using":[48],"SLM":[50,93],"UPenn":[53],"Treebank":[54],"Wall":[56],"Street":[57],"Journal":[58],"(WSJ)":[59],"corpora,":[60],"respectively.":[61],"Analysis":[62],"shows":[66],"correlation":[67],"between":[68],"quality":[70],"parser":[73],"(as":[74],"measured":[75],"by":[76,103],"precision/recall)":[77],"(PPL":[83],"WER).":[85],"A":[86],"remarkable":[87],"fact":[88],"is":[89],"enriched":[92],"outperforms":[94],"3-gram":[97],"terms":[100],"10%":[104],"when":[105],"used":[106],"isolation":[108],"as":[109],"a":[110],"second":[111],"pass":[112],"(N-best":[113],"re-scoring)":[114],"model.":[116]},"counts_by_year":[{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2015,"cited_by_count":4},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":5}],"updated_date":"2026-07-19T07:52:34.831488","created_date":"2025-10-10T00:00:00"}
