Titlebook: Representation Learning for Natural Language Processing; Zhiyuan Liu,Yankai Lin,Maosong Sun Book‘‘‘‘‘‘‘‘ 2023Latest edition The Editor(s)

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Word Representation Learning,s chapter, we introduce the approaches for word representation learning to show the paradigm shift from symbolic representation to distributed representation. We also describe the valuable efforts in making word representations more informative and interpretable. Finally, we present applications of

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Sentence and Document Representation Learning,lenging task because many important applications of natural language processing (NLP) lie in understanding sentences and documents. This chapter first introduces symbolic methods to sentence and document representation learning. Then we extensively introduce neural network-based methods for the far-

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Pre-trained Models for Representation Learning,ocuments in a self-supervised manner. Pre-trained models not only unify semantic representations of multiple tasks, multiple languages, and multiple modalities but also emerge high-level capabilities approaching human beings. In this chapter, we introduce pre-trained models for representation learni

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Graph Representation Learning,a sequence of word tokens, massive additional information in NLP is in the graph structure, such as syntactic relations between words in a sentence, hyperlink relations between documents, and semantic relations between entities. Hence, it is critical for NLP to encode these graph data with graph rep

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