{"type":"ScholarlyArticle","title":"Segmenting Chinese Microtext: Joint Informal-Word Detection and Segmentation with Neural Networks","authors":[{"name":"Meishan Zhang","slug":"meishan-zhang","given":"Meishan","family":"Zhang","affiliation":"School of Computer Science and Technology , Heilongjiang University , China"},{"name":"Guohong Fu","slug":"guohong-fu","given":"Guohong","family":"Fu","affiliation":"School of Computer Science and Technology , Heilongjiang University , China"},{"name":"Nan Yu","slug":"nan-yu","given":"Nan","family":"Yu","affiliation":"School of Computer Science and Technology , Heilongjiang University , China"}],"abstract":"State-of-the-art Chinese word segmentation systems typically exploit supervised modelstrained on a standard manually-annotated corpus,achieving performances over 95% on a similar standard testing corpus.However, the performances may drop significantly when the same models are applied onto Chinese microtext.One major challenge is the issue of informal words in the microtext.Previous studies show that informal word detection can be helpful for microtext processing.In this work, we investigate it under the neural setting, by proposing a joint segmentation model that integrates the detection of informal words simultaneously.In addition, we generate training corpus for the joint model by using existing corpus automatically.Experimental results show that the proposed model is highly effective for segmentation of Chinese microtext.","doi":"10.24963/ijcai.2017/591","url":"https://ijcai.org/proceedings/2017/591","pdf":"https://ijcai.org/proceedings/2017/0591.pdf","markdown":"https://ijcai.org/proceedings/2017/591.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/591","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Natural Language Processing","track":"Main track","number":591,"pages":{"first":4228,"last":4234},"keywords":["Natural Language Processing: Natural Language Processing","Machine Learning: Neural Networks","Natural Language Processing: Phonology, Morphology, and word segmentation"],"videos":[],"references":[{"position":1,"raw":"[Andor et al., 2016] Daniel Andor, Chris Alberti, David Weiss, Aliaksei Severyn, Alessandro Presta, Kuzman Ganchev, Slav Petrov, and Michael Collins. 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