Exploiting Language Models to Classify Events from TwitterReportar como inadecuado

Exploiting Language Models to Classify Events from Twitter - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

Computational Intelligence and Neuroscience - Volume 2015 2015, Article ID 401024, 11 pages -

Research Article

School of Electrical Engineering, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan 680-749, Republic of Korea

Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 156-743, Republic of Korea

Received 23 November 2014; Accepted 2 January 2015

Academic Editor: Weihui Dai

Copyright © 2015 Duc-Thuan Vo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.

Autor: Duc-Thuan Vo, Vo Thuan Hai, and Cheol-Young Ock

Fuente: https://www.hindawi.com/


Documentos relacionados