{"id":"https://openalex.org/W2603270557","doi":"https://doi.org/10.18653/v1/d17-1092","title":"Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture","display_name":"Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2603270557","doi":"https://doi.org/10.18653/v1/d17-1092","mag":"2603270557"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d17-1092","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d17-1092","pdf_url":"https://www.aclweb.org/anthology/D17-1092.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D17-1092.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001148852","display_name":"Yuanliang Meng","orcid":null},"institutions":[{"id":"https://openalex.org/I133738476","display_name":"University of Massachusetts Lowell","ror":"https://ror.org/03hamhx47","country_code":"US","type":"education","lineage":["https://openalex.org/I133738476"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuanliang Meng","raw_affiliation_strings":["Department of Computer Science University of Massachusetts Lowell Lowell, MA 01854"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science University of Massachusetts Lowell Lowell, MA 01854","institution_ids":["https://openalex.org/I133738476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071360545","display_name":"Anna Rumshisky","orcid":null},"institutions":[{"id":"https://openalex.org/I133738476","display_name":"University of Massachusetts Lowell","ror":"https://ror.org/03hamhx47","country_code":"US","type":"education","lineage":["https://openalex.org/I133738476"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anna Rumshisky","raw_affiliation_strings":["Department of Computer Science University of Massachusetts Lowell Lowell, MA 01854"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science University of Massachusetts Lowell Lowell, MA 01854","institution_ids":["https://openalex.org/I133738476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038929654","display_name":"Alexey Romanov","orcid":"https://orcid.org/0009-0004-0678-4456"},"institutions":[{"id":"https://openalex.org/I133738476","display_name":"University of Massachusetts Lowell","ror":"https://ror.org/03hamhx47","country_code":"US","type":"education","lineage":["https://openalex.org/I133738476"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexey Romanov","raw_affiliation_strings":["Department of Computer Science University of Massachusetts Lowell Lowell, MA 01854"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science University of Massachusetts Lowell Lowell, MA 01854","institution_ids":["https://openalex.org/I133738476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I133738476"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":65,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"887","last_page":"896"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9991000294685364,"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":0.9991000294685364,"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":0.9986000061035156,"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/T10215","display_name":"Semantic Web and Ontologies","score":0.9961000084877014,"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/computer-science","display_name":"Computer science","score":0.837836503982544},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.7130508422851562},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6577973365783691},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6464743614196777},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6219558119773865},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5767806172370911},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.5235616564750671},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.5116270184516907},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5037361979484558},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4354112446308136},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4281575083732605},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.4169846773147583},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.41535383462905884},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.265606552362442}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.837836503982544},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7130508422851562},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6577973365783691},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6464743614196777},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6219558119773865},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5767806172370911},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.5235616564750671},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.5116270184516907},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5037361979484558},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4354112446308136},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4281575083732605},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.4169846773147583},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.41535383462905884},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.265606552362442},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/v1/d17-1092","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d17-1092","pdf_url":"https://www.aclweb.org/anthology/D17-1092.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.ed.ac.uk:openaire/9e0baeb3-ef48-43c6-a738-1d00cc14880e","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/9e0baeb3-ef48-43c6-a738-1d00cc14880e","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Dong, L, Mallinson, J, Reddy, S & Lapata, M 2017, Learning to Paraphrase for Question Answering. in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. pp. 886\u2013897 , EMNLP 2017: Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, 7/09/17. https://doi.org/10.18653/v1/D17-1092","raw_type":"contributionToPeriodical"},{"id":"pmh:oai:pure.ed.ac.uk:publications/9e0baeb3-ef48-43c6-a738-1d00cc14880e","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/portal/en/publications/learning-to-paraphrase-for-question-answering(9e0baeb3-ef48-43c6-a738-1d00cc14880e).html","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""}],"best_oa_location":{"id":"doi:10.18653/v1/d17-1092","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d17-1092","pdf_url":"https://www.aclweb.org/anthology/D17-1092.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.44999998807907104,"display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G6671297155","display_name":null,"funder_award_id":"CAREER","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2603270557.pdf","grobid_xml":"https://content.openalex.org/works/W2603270557.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W30314283","https://openalex.org/W1571872475","https://openalex.org/W1838058638","https://openalex.org/W1990886313","https://openalex.org/W2058070641","https://openalex.org/W2064675550","https://openalex.org/W2112251443","https://openalex.org/W2114117930","https://openalex.org/W2128536089","https://openalex.org/W2137407193","https://openalex.org/W2140244223","https://openalex.org/W2141539902","https://openalex.org/W2185599447","https://openalex.org/W2250521169","https://openalex.org/W2251220668","https://openalex.org/W2251463950","https://openalex.org/W2401610261","https://openalex.org/W2468432491","https://openalex.org/W2571747590","https://openalex.org/W2962785888","https://openalex.org/W2964217331","https://openalex.org/W3149604617","https://openalex.org/W4302411194"],"related_works":["https://openalex.org/W1984061923","https://openalex.org/W2521472864","https://openalex.org/W2948022516","https://openalex.org/W2004087619","https://openalex.org/W2469016277","https://openalex.org/W2557094866","https://openalex.org/W2757101400","https://openalex.org/W2362196274","https://openalex.org/W1982302668","https://openalex.org/W1990527953"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"to":[5,16,39],"use":[6],"a":[7,88],"set":[8],"of":[9,20,59],"simple,":[10],"uniform":[11],"in":[12,54],"architecture":[13,36],"LSTMbased":[14],"models":[15],"recover":[17],"different":[18],"kinds":[19],"temporal":[21],"relations":[22],"from":[23],"text.":[24],"Using":[25],"the":[26,34,57],"shortest":[27],"dependency":[28],"path":[29],"between":[30,65],"entities":[31],"as":[32],"input,":[33],"same":[35],"is":[37],"implemented":[38],"extract":[40],"intra-sentence,":[41],"crosssentence,":[42],"and":[43,62,96],"document":[44],"creation":[45],"time":[46],"relations.":[47],"A":[48],"\"double-checking\"":[49],"technique":[50,83],"reverses":[51],"entity":[52],"pairs":[53],"classification,":[55],"boosting":[56],"recall":[58],"positive":[60],"cases":[61],"reducing":[63],"misclassifications":[64],"opposite":[66],"classes.":[67],"An":[68],"efficient":[69],"pruning":[70],"algorithm":[71],"resolves":[72],"conflicts":[73],"globally.":[74],"Evaluated":[75],"on":[76,100],"QA-TempEval":[77],"(SemEval2015":[78],"Task":[79],"5),":[80],"our":[81],"proposed":[82],"outperforms":[84],"state-ofthe-art":[85],"methods":[86],"by":[87],"large":[89],"margin.":[90],"We":[91],"also":[92],"conduct":[93],"intrinsic":[94],"evaluation":[95],"post":[97],"stateof-the-art":[98],"results":[99],"Timebank-Dense.":[101]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":15},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":13},{"year":2018,"cited_by_count":5}],"updated_date":"2026-07-19T07:52:34.831488","created_date":"2025-10-10T00:00:00"}
