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Eng."],"published-print":{"date-parts":[[2019,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In this work, we focus on the task of <jats:italic>open-type relation argument extraction (ORAE)<\/jats:italic>: given a corpus, a query entity <jats:italic>Q<\/jats:italic>, and a knowledge base relation (e.g., \u201c<jats:italic>Q<\/jats:italic> authored notable work with title <jats:italic>X<\/jats:italic>\u201d), the model has to extract an argument of non-standard entity type (entities that cannot be extracted by a standard named entity tagger, for example, <jats:italic>X<\/jats:italic>: the title of a book or a work of art) from the corpus. We develop and compare a wide range of neural models for this task yielding large improvements over a strong baseline obtained with a neural question answering system. The impact of different sentence encoding architectures and answer extraction methods is systematically compared. An encoder based on gated recurrent units combined with a conditional random fields tagger yields the best results. We release a data set to train and evaluate ORAE, based on Wikidata and obtained by distant supervision.<\/jats:p>","DOI":"10.1017\/s1351324918000451","type":"journal-article","created":{"date-parts":[[2018,12,7]],"date-time":"2018-12-07T03:42:16Z","timestamp":1544154136000},"page":"219-238","source":"Crossref","is-referenced-by-count":3,"title":["Neural architectures for open-type relation argument extraction"],"prefix":"10.1017","volume":"25","author":[{"given":"Benjamin","family":"Roth","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Costanza","family":"Conforti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nina","family":"Poerner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjeev Kumar","family":"Karn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hinrich","family":"Sch\u00fctze","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2018,12,7]]},"reference":[{"key":"S1351324918000451_ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35289-8_26"},{"key":"S1351324918000451_ref42","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1004"},{"key":"S1351324918000451_ref39","first-page":"1461","volume-title":"Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers","author":"Xu","year":"2016"},{"key":"S1351324918000451_ref41","volume-title":"Proceedings of TAC","author":"Zhang","year":"2016"},{"key":"S1351324918000451_ref40","first-page":"2335","volume-title":"Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers","author":"Zeng","year":"2014"},{"key":"S1351324918000451_ref36","first-page":"2692","volume-title":"Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems","author":"Vinyals","year":"2015"},{"key":"S1351324918000451_ref34","article-title":"Attention is all you need","author":"Vaswani","year":"2017","journal-title":"CoRR"},{"key":"S1351324918000451_ref33","volume-title":"Proceedings of the Sixth Text Analysis Conference, TAC 2013","author":"Surdeanu","year":"2013"},{"key":"S1351324918000451_ref32","article-title":"Bidirectional attention flow for machine comprehension","author":"Seo","year":"2016","journal-title":"CoRR"},{"key":"S1351324918000451_ref26","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"S1351324918000451_ref43","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1113"},{"key":"S1351324918000451_ref25","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1200"},{"key":"S1351324918000451_ref13","first-page":"140","volume-title":"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics","volume":"1","author":"G\u00fcl\u00e7ehre","year":"2016"},{"key":"S1351324918000451_ref22","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/K17-1034"},{"key":"S1351324918000451_ref38","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1018"},{"key":"S1351324918000451_ref14","first-page":"2537","volume-title":"Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers","author":"Gupta","year":"2016"},{"key":"S1351324918000451_ref10","first-page":"2493","article-title":"Natural language processing (almost) from scratch","volume":"12","author":"Collobert","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"S1351324918000451_ref30","unstructured":"Roth B. 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