Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2018

Abstract

Uncertainty identification is an important semantic processing task, which is crucial to the quality of information in terms of factuality in many techniques, e.g. topic detection, question answering. Especially in social media, the texts are written informally which are widely used in many applications, so the factuality has become a premier concern. However, existing approaches that still rely on lexical cues suffer greatly from the casual or word-of-mouth peculiarity of social media, in which the cue phrases are often expressed in sub-standard form or even omitted from sentences. To tackle these problems, this paper proposes the attention-based LSTM-CNNs for the uncertainty identification on social media texts, named ALUNI. ALUNI incorporates attention-based LSTM networks to represent the semantics of words, and convolutional neural networks to capture the most important semantics of uncertainty for identification. Experiments are conducted on both Chinese Weibo and news datasets, and 78.19% and 73.95% of F1-measure scores are achieved with 11% and 3% improvement over the baseline, respectively.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2017 International Conference on Security, Pattern Analysis and Cybernetics (SPAC 2017)

First Page

609

Last Page

614

Identifier

10.1109/SPAC.2017.8304349

Publisher

IEEE Press

City or Country

Shenzhen, China

Additional URL

https://doi.org/10.1109/SPAC.2017.8304349

Download

Share

COinS