{"type":"ScholarlyArticle","title":"HC-Search for Incremental Parsing","authors":[{"name":"Yijia Liu","slug":"yijia-liu","given":"Yijia","family":"Liu","affiliation":""},{"name":"Wanxiang Che","slug":"wanxiang-che","given":"Wanxiang","family":"Che","affiliation":""},{"name":"Bing Qin","slug":"bing-qin","given":"Bing","family":"Qin","affiliation":""},{"name":"Ting Liu","slug":"ting-liu","given":"Ting","family":"Liu","affiliation":""}],"abstract":"Standard incremental parsing algorithm employs a single scoring function and beam-search to find the best parse tree from an exponentially large search space. Inspired by recently proposed HC-search framework, we decompose the incremental parsing algorithm into two steps: first searching a set of high-quality outputs with beam-search, and second selecting the best output with a ranking model. We learn our incremental parsing model with a relaxed learning objective. We incorporate arbitrary features in our ranking model and learn the model from fine grain ranking examples. Experimental results on standard English and Chinese datasets show our method significantly outperforms a strong baseline.","doi":null,"url":"https://ijcai.org/proceedings/2016/410","pdf":"https://ijcai.org/proceedings/2016/0410.pdf","markdown":"https://ijcai.org/proceedings/2016/410.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/410","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":410,"pages":{"first":2887,"last":2892},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}