Representation Learning for Natural Language Processing

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains

Chinese LIWC Lexicon Expansion via Hierarchical Classification of Word Embeddings with Sememe Attention

Linguistic Inquiry and Word Count (LIWC) is a word counting software tool which has been used for quantitative text analysis in many fields. Due to its success and popularity, the core lexicon has been translated into Chinese and many other languages. However, the lexicon only contains several thousand of words, which is deficient compared with the number of common words in Chinese. Current approaches often require manually expanding the lexicon, but it often takes too much time and requires linguistic experts to extend the lexicon. To address this issue, we propose to expand the LIWC lexicon automatically. Specifically, we consider it as a hierarchical classification problem and utilize the Sequence-to-Sequence model to classify words in the lexicon. Moreover, we use the sememe information with the attention mechanism to capture the exact meanings of a word, so that we can expand a more precise and comprehensive lexicon. The experimental results show that our model has a better understanding of word meanings with the help of sememes and achieves significant and consistent improvements compared with the state-of-the-art methods. The source code of this paper can be obtained from https://github.com/thunlp/Auto_CLIWC