Event Factuality Detection in Discourse

Abstract

Event factuality indicates whether an event occurs or the degree of certainty described by authors in context. Correctly identifying event factuality in texts can contribute to a deep understanding of natural language. In addition, event factuality detection is of great significance to many natural language processing applications, such as opinion detection, emotional reasoning, and public opinion analysis. Existing studies mainly focus on identifying event factuality by the features in the current sentence (e.g. negation or modality). However, there might be many different descriptions of factuality in a document, corresponding to the same event. It leads to conflict when identifying event factuality only on sentence level. To address such issues, we come up with a document-level approach on event factuality detection, which employs Bi-directional Long Short-Term Memory (BiLSTM) neural networks to learn contextual information of the event in sentences. Moreover, we utilize a double-layer attention mechanism to capture the latent correlation features among event sequences in the discourse, and identify event factuality according to the whole document. The experimental results on both English and Chinese event factuality detection datasets demonstrate the effectiveness of our approach. The performances of the proposed system achieved 86.67% and 86.97% of F1 scores, yielding improvements of 3.24% and 4.78% over the state-of-the-art on English and Chinese datasets, respectively.