Attention-based LSTM-CNNs for uncertainty identification on Chinese social media texts

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.