{"id":"https://openalex.org/W2143170264","doi":"https://doi.org/10.1145/1553374.1553466","title":"Polyhedral outer approximations with application to natural language parsing","display_name":"Polyhedral outer approximations with application to natural language parsing","publication_year":2009,"publication_date":"2009-06-14","ids":{"openalex":"https://openalex.org/W2143170264","doi":"https://doi.org/10.1145/1553374.1553466","mag":"2143170264"},"language":"en","primary_location":{"id":"doi:10.1145/1553374.1553466","doi":"https://doi.org/10.1145/1553374.1553466","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1553374.1553466","pdf_url":null,"source":{"id":"https://openalex.org/S4306419644","display_name":"International Conference on Machine 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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th Annual International Conference on Machine Learning","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5051693368","display_name":"Andr\u00e9 F. T. Martins","orcid":"https://orcid.org/0000-0001-6863-4681","observed_orcids":["https://orcid.org/0000-0001-6863-4681"]},"institutions":[{"id":"https://openalex.org/I4387152517","display_name":"Instituto Superior T\u00e9cnico","ror":"https://ror.org/03db2by73","country_code":"PT","type":"education","lineage":["https://openalex.org/I141596103","https://openalex.org/I4387152517"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["PT","US"],"is_corresponding":false,"raw_author_name":"Andr\u00e9 F. T. Martins","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA and Instituto Superior T\u00e9cnico, Lisboa, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA and Instituto Superior T\u00e9cnico, Lisboa, Portugal","institution_ids":["https://openalex.org/I4387152517","https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088517824","display_name":"Noah A. Smith","orcid":"https://orcid.org/0000-0002-2310-6380","observed_orcids":["https://orcid.org/0000-0002-2310-6380"]},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Noah A. Smith","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009547049","display_name":"Eric P. Xing","orcid":"https://orcid.org/0009-0005-9158-4201","observed_orcids":["https://orcid.org/0009-0005-9158-4201","https://orcid.org/0000-0002-3683-4280"]},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eric P. Xing","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.3911,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":{"value":0.95723159,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"713","last_page":"720"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.8906000256538391,"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":0.8906000256538391,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.07999999821186066,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.006300000008195639,"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/linear-programming-relaxation","display_name":"linear programming relaxation","score":0.9330000281333923},{"id":"https://openalex.org/keywords/max-margin-learning","display_name":"max-margin learning","score":0.871999979019165},{"id":"https://openalex.org/keywords/risk-bounds","display_name":"risk bounds","score":0.8769999742507935},{"id":"https://openalex.org/keywords/approximate-inference","display_name":"approximate inference","score":0.8130000233650208},{"id":"https://openalex.org/keywords/dependency-parsing","display_name":"dependency parsing","score":0.8339999914169312},{"id":"https://openalex.org/keywords/natural-language-parsing","display_name":"natural language parsing","score":0.8119999766349792},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"structured prediction","score":0.6869999766349792}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7572797536849976},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.7134801149368286},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6828210353851318},{"id":"https://openalex.org/C54829058","wikidata":"https://www.wikidata.org/wiki/Q172937","display_name":"Polyhedron","level":2,"score":0.6163710951805115},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.6062307357788086},{"id":"https://openalex.org/C25360446","wikidata":"https://www.wikidata.org/wiki/Q1512771","display_name":"Linear programming relaxation","level":3,"score":0.5370069742202759},{"id":"https://openalex.org/C2776029896","wikidata":"https://www.wikidata.org/wiki/Q3935810","display_name":"Relaxation (psychology)","level":2,"score":0.534379243850708},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.5301559567451477},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5244160890579224},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5032095313072205},{"id":"https://openalex.org/C2777027219","wikidata":"https://www.wikidata.org/wiki/Q1284190","display_name":"Constant (computer programming)","level":2,"score":0.4429706335067749},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.39104706048965454},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3649415373802185},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32301753759384155},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2047276496887207},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.19989854097366333},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.19211849570274353},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.10058075189590454},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/1553374.1553466","doi":"https://doi.org/10.1145/1553374.1553466","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1553374.1553466","pdf_url":null,"source":{"id":"https://openalex.org/S4306419644","display_name":"International Conference on Machine 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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th Annual International Conference on Machine Learning","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.149.8332","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.8332","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.mcgill.ca/~icml2009/papers/175.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.180.9387","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.180.9387","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.cmu.edu/%7Enasmith/papers/martins%2Bsmith%2Bxing.icml09.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.207.6650","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.207.6650","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.cmu.edu/%7Eafm/Home_files/icml2009.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G4062411421","display_name":null,"funder_award_id":"IIS-0836431DBI-0546594DBI-0640543IIS-0713379","funder_id":"https://openalex.org/F4320337389","funder_display_name":"Division of Information and Intelligent Systems"},{"id":"https://openalex.org/G5282020284","display_name":null,"funder_award_id":"IIS-0836431DBI-0546594DBI-0640543IIS-0713379","funder_id":"https://openalex.org/F4320337398","funder_display_name":"Division of Biological Infrastructure"}],"funders":[{"id":"https://openalex.org/F4320337389","display_name":"Division of Information and Intelligent Systems","ror":"https://ror.org/053a2cp42"},{"id":"https://openalex.org/F4320337398","display_name":"Division of Biological Infrastructure","ror":"https://ror.org/04qn9mx93"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W194033037","https://openalex.org/W1977970897","https://openalex.org/W1986500605","https://openalex.org/W1989115577","https://openalex.org/W2008652694","https://openalex.org/W2010624529","https://openalex.org/W2027979924","https://openalex.org/W2052449326","https://openalex.org/W2074078071","https://openalex.org/W2092423930","https://openalex.org/W2100008268","https://openalex.org/W2105644991","https://openalex.org/W2106122512","https://openalex.org/W2109339818","https://openalex.org/W2115322217","https://openalex.org/W2117822433","https://openalex.org/W2122060553","https://openalex.org/W2122922578","https://openalex.org/W2130031580","https://openalex.org/W2136646623","https://openalex.org/W2141732516","https://openalex.org/W2142603219","https://openalex.org/W2143541936","https://openalex.org/W2147880316","https://openalex.org/W2159992248","https://openalex.org/W2160218441","https://openalex.org/W2161002641","https://openalex.org/W2429914308","https://openalex.org/W2541794668","https://openalex.org/W2611804663","https://openalex.org/W2612953450","https://openalex.org/W3009009611","https://openalex.org/W4246185962"],"related_works":["https://openalex.org/W147273232","https://openalex.org/W7096157700","https://openalex.org/W2343505200","https://openalex.org/W2142603219","https://openalex.org/W144183602","https://openalex.org/W7096268584","https://openalex.org/W7099252769","https://openalex.org/W1989115577","https://openalex.org/W7097633393","https://openalex.org/W2182299232"],"abstract_inverted_index":{"Recent":[0],"approaches":[1],"to":[2,68,93],"learning":[3,23,51],"structured":[4],"predictors":[5],"often":[6],"require":[7],"approximate":[8],"inference":[9,55],"for":[10,49,99],"tractability;":[11],"yet":[12],"its":[13],"effects":[14],"on":[15,41,75],"the":[16,33,42,58,80,85,94],"learned":[17],"model":[18,34],"are":[19],"unclear.":[20],"Meanwhile,":[21],"most":[22],"algorithms":[24],"act":[25],"as":[26],"if":[27],"computational":[28],"cost":[29],"was":[30],"constant":[31],"within":[32],"class.":[35],"This":[36],"paper":[37],"sheds":[38],"some":[39],"light":[40],"first":[43],"issue":[44,60],"by":[45,61],"establishing":[46],"risk":[47],"bounds":[48],"max-margin":[50],"with":[52,84],"LP":[53,86,103],"relaxed":[54],"and":[56],"addresses":[57],"second":[59],"proposing":[62],"a":[63,76,101],"new":[64],"paradigm":[65],"that":[66,107],"attempts":[67],"penalize":[69],"\u201ctimeconsuming\u201d":[70],"hypotheses.":[71],"Our":[72],"analysis":[73],"relies":[74],"geometric":[77],"characterization":[78],"of":[79,96],"outer":[81],"polyhedra":[82],"associated":[83],"relaxation.":[87],"We":[88],"then":[89],"apply":[90],"these":[91],"techniques":[92],"problem":[95],"dependency":[97],"parsing,":[98],"which":[100],"concise":[102],"formulation":[104],"is":[105,115],"provided":[106],"handles":[108],"non-local":[109],"output":[110],"features.":[111],"A":[112],"significant":[113],"improvement":[114],"shown":[116],"over":[117],"arc-factored":[118],"models.":[119],"1.":[120]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":6},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":7}],"updated_date":"2026-11-05T12:25:35.769452","created_date":"2016-06-24T00:00:00"}
