{"id":"https://openalex.org/W2903601707","doi":null,"title":"Deep learning applications for transition-based dependency parsing","display_name":"Deep learning applications for transition-based dependency parsing","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2903601707","mag":"2903601707"},"language":"en","primary_location":{"id":"pmh:oai:etheses.bham.ac.uk:8620","doi":null,"is_oa":true,"landing_page_url":"http://etheses.bham.ac.uk/8620/1/Elkaref18PhD.pdf","pdf_url":"http://etheses.bham.ac.uk/8620/1/Elkaref18PhD.pdf","source":{"id":"https://openalex.org/S4306402357","display_name":"University of Birmingham Institutional Research Archive (University of Birmingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I79619799","host_organization_name":"University of Birmingham","host_organization_lineage":["https://openalex.org/I79619799"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Thesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://etheses.bham.ac.uk/8620/1/Elkaref18PhD.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000890126","display_name":"Mohab Elkaref","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Mohab Elkaref","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5000890126"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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":0.9980000257492065,"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":0.9980000257492065,"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":0.0008999999845400453,"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":0.00019999999494757503,"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/dependency-trees","display_name":"dependency trees","score":0.7710000276565552},{"id":"https://openalex.org/keywords/recurrent-neural-networks","display_name":"recurrent neural networks","score":0.8880000114440918},{"id":"https://openalex.org/keywords/tree-representation","display_name":"tree representation","score":0.7120000123977661},{"id":"https://openalex.org/keywords/syntactic-analysis","display_name":"syntactic analysis","score":0.46799999475479126}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8455936312675476},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.8128544092178345},{"id":"https://openalex.org/C164883195","wikidata":"https://www.wikidata.org/wiki/Q674834","display_name":"Dependency grammar","level":3,"score":0.7468140125274658},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7179641127586365},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.6440464854240417},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5544344186782837},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5537480115890503},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5450792908668518},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5009927749633789},{"id":"https://openalex.org/C60690694","wikidata":"https://www.wikidata.org/wiki/Q894902","display_name":"Bottom-up parsing","level":4,"score":0.4768701195716858},{"id":"https://openalex.org/C42560504","wikidata":"https://www.wikidata.org/wiki/Q15419395","display_name":"Top-down parsing","level":3,"score":0.4627611041069031},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.45385345816612244},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.4381192922592163},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4284003674983978},{"id":"https://openalex.org/C118364021","wikidata":"https://www.wikidata.org/wiki/Q7139956","display_name":"Parser combinator","level":3,"score":0.4206623435020447},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3255111575126648},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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},{"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/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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":"pmh:oai:etheses.bham.ac.uk:8620","doi":null,"is_oa":true,"landing_page_url":"http://etheses.bham.ac.uk/8620/1/Elkaref18PhD.pdf","pdf_url":"http://etheses.bham.ac.uk/8620/1/Elkaref18PhD.pdf","source":{"id":"https://openalex.org/S4306402357","display_name":"University of Birmingham Institutional Research Archive (University of Birmingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I79619799","host_organization_name":"University of Birmingham","host_organization_lineage":["https://openalex.org/I79619799"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Thesis"},{"id":"mag:2903601707","doi":null,"is_oa":false,"landing_page_url":"http://etheses.bham.ac.uk/id/eprint/8620","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:etheses.bham.ac.uk:8620","doi":null,"is_oa":true,"landing_page_url":"http://etheses.bham.ac.uk/8620/1/Elkaref18PhD.pdf","pdf_url":"http://etheses.bham.ac.uk/8620/1/Elkaref18PhD.pdf","source":{"id":"https://openalex.org/S4306402357","display_name":"University of Birmingham Institutional Research Archive (University of Birmingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I79619799","host_organization_name":"University of Birmingham","host_organization_lineage":["https://openalex.org/I79619799"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Thesis"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2903601707.pdf","grobid_xml":"https://content.openalex.org/works/W2903601707.grobid-xml"},"referenced_works_count":67,"referenced_works":["https://openalex.org/W104184427","https://openalex.org/W140467209","https://openalex.org/W181643614","https://openalex.org/W270613446","https://openalex.org/W581956982","https://openalex.org/W1508977358","https://openalex.org/W1515383068","https://openalex.org/W1533861849","https://openalex.org/W1539312853","https://openalex.org/W1541975828","https://openalex.org/W1569080657","https://openalex.org/W1591801644","https://openalex.org/W1632114991","https://openalex.org/W1800356822","https://openalex.org/W1810943226","https://openalex.org/W1904365287","https://openalex.org/W1924770834","https://openalex.org/W1955857676","https://openalex.org/W2027979924","https://openalex.org/W2030904529","https://openalex.org/W2064675550","https://openalex.org/W2099162170","https://openalex.org/W2104246439","https://openalex.org/W2104518905","https://openalex.org/W2104917081","https://openalex.org/W2105847779","https://openalex.org/W2106310992","https://openalex.org/W2107878631","https://openalex.org/W2108747161","https://openalex.org/W2110485445","https://openalex.org/W2116410915","https://openalex.org/W2117130368","https://openalex.org/W2118776487","https://openalex.org/W2122922578","https://openalex.org/W2129842875","https://openalex.org/W2129879227","https://openalex.org/W2134036914","https://openalex.org/W2136848157","https://openalex.org/W2137132216","https://openalex.org/W2142708806","https://openalex.org/W2143541936","https://openalex.org/W2147568880","https://openalex.org/W2156431679","https://openalex.org/W2212703438","https://openalex.org/W2250405765","https://openalex.org/W2250539671","https://openalex.org/W2250861254","https://openalex.org/W2251433671","https://openalex.org/W2251628756","https://openalex.org/W2251913848","https://openalex.org/W2290523257","https://openalex.org/W2294621531","https://openalex.org/W2301095666","https://openalex.org/W2301603631","https://openalex.org/W2311132329","https://openalex.org/W2345157264","https://openalex.org/W2467375345","https://openalex.org/W2554915555","https://openalex.org/W2556468274","https://openalex.org/W2569308312","https://openalex.org/W2577255746","https://openalex.org/W2950635152","https://openalex.org/W2951588135","https://openalex.org/W2963135511","https://openalex.org/W2963481229","https://openalex.org/W2964121744","https://openalex.org/W2964289395"],"related_works":["https://openalex.org/W2345157264","https://openalex.org/W2423636506","https://openalex.org/W3001662750","https://openalex.org/W2114887620","https://openalex.org/W2949952998","https://openalex.org/W2486884974","https://openalex.org/W2750654012","https://openalex.org/W2400208159","https://openalex.org/W2951588135","https://openalex.org/W2330236085"],"abstract_inverted_index":{"Dependency":[0],"Parsing":[1],"is":[2,56,78],"a":[3,57,106,132,144,166],"method":[4],"that":[5,13,94,123,143,179],"builds":[6],"dependency":[7,23,127],"trees":[8],"consisting":[9],"of":[10,18,49,88,188],"binary":[11],"relations":[12],"describe":[14],"the":[15,47,82,86,117,137],"syntactic":[16],"role":[17],"words":[19],"in":[20,182],"sentences.":[21],"Recently,":[22],"parsing":[24,128],"has":[25],"seen":[26],"large":[27],"improvements":[28],"due":[29],"to":[30,52,67,194],"deep":[31],"learning,":[32],"which":[33,55,159],"enabled":[34],"richer":[35],"feature":[36],"representations":[37],"and":[38,64,72,129,146,170,191],"flexible":[39],"architectures.":[40],"In":[41],"this":[42,183,189],"thesis":[43,184],"we":[44,115,154,180],"focus":[45],"on":[46,81],"application":[48],"these":[50],"methods":[51],"Transition-based":[53],"parsing,":[54],"faster":[58],"variant.":[59],"We":[60,92,121,140],"explore":[61],"current":[62,90,197],"architectures":[63],"examine":[65,116],"ways":[66],"improve":[68],"their":[69],"representation":[70],"capabilities":[71],"final":[73],"accuracies.":[74],"Our":[75],"first":[76],"contribution":[77],"an":[79,162],"improvement":[80],"basic":[83],"architecture":[84],"at":[85],"heart":[87],"many":[89,186],"parsers.":[91],"show":[93,122,142],"using":[95],"Recurrent":[96],"Neural":[97],"Network":[98],"hidden":[99],"layers,":[100],"initialised":[101],"with":[102,131,174,196],"pretrained":[103],"weights":[104],"from":[105],"feed":[107],"forward":[108],"network,":[109],"provides":[110,136],"significant":[111],"accuracy":[112,173],"improvements.":[113],"Second,":[114],"best":[118,138],"parser":[119,145],"architecture.":[120],"separate":[124],"classifiers":[125],"for":[126],"labelling,":[130],"shared":[133],"input":[134],"layer":[135],"accuracy.":[139],"also":[141],"labeller":[147],"can":[148,160],"be":[149],"successfully":[150],"trained":[151],"separately.":[152],"Finally,":[153],"propose":[155],"Recursive":[156],"LSTM":[157],"Trees,":[158],"represent":[161],"entire":[163],"tree":[164],"as":[165],"single":[167],"dense":[168],"vector,":[169],"achieve":[171],"competitive":[172],"minimal":[175],"features.":[176],"The":[177],"parsers":[178],"develop":[181],"cover":[185],"aspects":[187],"task,":[190],"are":[192],"easy":[193],"integrate":[195],"methods.":[198]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2018-12-22T00:00:00"}
