title: Exploiting Language Models to Classify Events from Twitter.
abstract: Classifying events is challenging in Twitter because tweets texts have a large amount of temporal data with a lot of noise and various kinds of topics. In this paper, we propose a method to classify events from Twitter. We firstly find the distinguishing terms between tweets in events and measure their similarities with learning language models such as ConceptNet and a latent Dirichlet allocation method for selectional preferences (LDA-SP), which have been widely studied based on large text corpora within computational linguistic relations. The relationship of term words in tweets will be discovered by checking them under each model. We then proposed a method to compute the similarity between tweets based on tweets' features including common term words and relationships among their distinguishing term words. It will be explicit and convenient for applying to k-nearest neighbor techniques for classification. We carefully applied experiments on the Edinburgh Twitter Corpus to show that our method achieves competitive results for classifying events.
authors.affiliation: School of Electrical Engineering, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan 680-749, Republic of Korea.
authors.forename: Duc-Thuan
authors.initials: DT
authors.lastname: Vo
authors.affiliation: Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 156-743, Republic of Korea.
authors.forename: Vo Thuan
authors.initials: VT
authors.lastname: Hai
authors.affiliation: School of Electrical Engineering, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan 680-749, Republic of Korea.
authors.forename: Cheol-Young
authors.initials: CY
authors.lastname: Ock
mesh_terms: D006801:Humans; D033181:Information Dissemination; D000069550:Machine Learning; D008960:Models, Psychological; D009323:Natural Language Processing; D061108:Social Media