{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T14:09:35Z","timestamp":1768486175377,"version":"3.49.0"},"reference-count":4,"publisher":"China Science Publishing & Media Ltd.","issue":"2","content-domain":{"domain":["www.mitpressjournals.org"],"crossmark-restriction":true},"short-container-title":["Data Intellegence"],"published-print":{"date-parts":[[2019,5]]},"abstract":"<jats:p> We propose a new framework for entity and event extraction based on generative adversarial imitation learning\u2014an inverse reinforcement learning method using a generative adversarial network (GAN). We assume that instances and labels yield to various extents of difficulty and the gains and penalties (rewards) are expected to be diverse. We utilize discriminators to estimate proper rewards according to the difference between the labels committed by the ground-truth (expert) and the extractor (agent). Our experiments demonstrate that the proposed framework outperforms state-of-the-art methods. <\/jats:p>","DOI":"10.1162\/dint_a_00014","type":"journal-article","created":{"date-parts":[[2019,4,29]],"date-time":"2019-04-29T12:49:27Z","timestamp":1556542167000},"page":"99-120","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":65,"title":["Joint Entity and Event Extraction with Generative Adversarial Imitation Learning"],"prefix":"10.3724","volume":"1","author":[{"given":"Tongtao","family":"Zhang","sequence":"first","affiliation":[{"name":"Computer Science Department, Rensselaer Polytechnic Institute, Troy, New York 12180-3590, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7954-7994","authenticated-orcid":true,"given":"Heng","family":"Ji","sequence":"additional","affiliation":[{"name":"Computer Science Department, Rensselaer Polytechnic Institute, Troy, New York 12180-3590, USA"}]},{"given":"Avirup","family":"Sil","sequence":"additional","affiliation":[{"name":"IBM Research Al, Armonk, New York 10504-1722, USA"}]}],"member":"2026","reference":[{"key":"ref1","volume-title":"ACE 2005 multilingual training corpus","author":"Walker C.","year":"2006"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5106-x"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref36","first-page":"1057","volume-title":"Advances in Neural Information Processing Systems 12 (NIPS 1999)","author":"Sutton R.S.","year":"2000"}],"container-title":["Data Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mitpressjournals.org\/doi\/pdf\/10.1162\/dint_a_00014","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T07:42:58Z","timestamp":1741938178000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.sciengine.com\/doi\/10.1162\/dint_a_00014"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5]]},"references-count":4,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,5]]}},"alternative-id":["10.1162\/dint_a_00014"],"URL":"https:\/\/doi.org\/10.1162\/dint_a_00014","relation":{},"ISSN":["2641-435X"],"issn-type":[{"value":"2641-435X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5]]},"assertion":[{"value":"2018-12-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-02-19","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-04-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}