{"type":"ScholarlyArticle","title":"Open Information Extraction Systems and Downstream Applications","authors":[{"name":"Mausam","slug":"mausam","given":"","family":"Mausam","affiliation":""}],"abstract":"Open Information Extraction (Open IE) extracts textual tuples comprising relation phrases and argument phrases from within a sentence, without requiring a pre-specified relation vocabulary. In this paper we first describe a decade of our progress on building Open IE extractors, which results in our latest extractor, OpenIE4, which is computationally efficient, outputs n-ary and nested relations, and also outputs relations mediated by nouns in addition to verbs. We also identify several strengths of the Open IE paradigm, which enable it to be a useful intermediate structure for end tasks. We survey its use in both human-facing applications and downstream NLP tasks, including event schema induction, sentence similarity, text comprehension, learning word vector embeddings, and more.","doi":null,"url":"https://ijcai.org/proceedings/2016/604","pdf":"https://ijcai.org/proceedings/2016/0604.pdf","markdown":"https://ijcai.org/proceedings/2016/604.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/604","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":604,"pages":{"first":4074,"last":4076},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}