Deep Learning for Natural Language Processing
Yuan Wang, Zekun Li, Zhenyu Deng, Huiling Song, Jucheng Yang
Abstract
With the constantly growing number of topical or sentiment-bearing texts and dialogs on the Web, the demand for automatic language or text analysis algorithms continues to expand. This chapter discusses about advanced deep learning techniques for classical and hot research directions in the field of natural language processing, including text classification, sentiment analysis, and task-oriented dialog systems. In text classification, we focus on tasks of multi-label text classification and extreme multi-label text classification, which allow for automatically annotates the texts with the most relevant labels. In sentiment analysis, we look into aspect-based sentiment analysis that makes automatic extraction of fine-grained sentiment information from texts, and multimodal sentiment analysis that classifies people’s opinions or attitudes from multimedia data through fusion techniques. In dialog system, we introduce how deep learning techniques work in pipeline mode and end-to-end mode for task-oriented dialog system. In this chapter, the rapidly evolving state of the research on the three topics is reviewed. Furthermore, trends in the research on deep learning for natural language processing are identified, and a discussion about future advances is provided.