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    Representation Learning for Natural Language Processing

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    Contributor(s)
    Liu, Zhiyuan (editor)
    Lin, Yankai (editor)
    Sun, Maosong (editor)
    Language
    English
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    Abstract
    This book provides an overview of the recent advances in representation learning theory, algorithms, and applications for natural language processing (NLP), ranging from word embeddings to pre-trained language models. It is divided into four parts. Part I presents the representation learning techniques for multiple language entries, including words, sentences and documents, as well as pre-training techniques. Part II then introduces the related representation techniques to NLP, including graphs, cross-modal entries, and robustness. Part III then introduces the representation techniques for the knowledge that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, legal domain knowledge and biomedical domain knowledge. Lastly, Part IV 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. As compared to the first edition, the second edition (1) provides a more detailed introduction to representation learning in Chapter 1; (2) adds four new chapters to introduce pre-trained language models, robust representation learning, legal knowledge representation learning and biomedical knowledge representation learning; (3) updates recent advances in representation learning in all chapters; and (4) corrects some errors in the first edition. The new contents will be approximately 50%+ compared to the first edition. This is an open access book.
    URI
    https://library.oapen.org/handle/20.500.12657/76271
    Keywords
    Deep Learning; Representation Learning; Knowledge Representation; Word Representation; Document Representation; Big Data; Machine Learning; Natural Language Processing; Artificial Intelligence
    DOI
    10.1007/978-981-99-1600-9
    ISBN
    9789819916009, 9789819915996, 9789819916009
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    Singapore, 2023
    Grantor
    • Tsinghua University - [...]
    Imprint
    Springer Nature Singapore
    Pages
    521
    Rights
    http://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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