An Attention Based Neural Framework for Uncertainty Identification on Social Media Texts in Python

Uncertainty identification is an important semantic processing task. It is crucial to the quality of information in terms of factuality in many applications, such as topic detection and question answering. Factuality has become a premier concern especially in social media, in which texts are written informally. However, existing approaches that 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 substandard form or even omitted from sentences. To tackle these problems, this paper proposes an Attention-based Neural Framework for Uncertainty identification on social media texts, named ANFU. ANFU incorporates attention-based Long Short-Term Memory (LSTM) networks to represent the semantics of words and Convolutional Neural Networks (CNNs) to capture the most important semantics.