TY - BOOK ID - 119428521 TI - Representation Learning for Natural Language Processing AU - Liu, Zhiyuan. AU - Lin, Yankai. AU - Sun, Maosong. PY - 2023 SN - 9819916003 9819915996 PB - Singapore : Springer Nature Singapore : Imprint: Springer, DB - UniCat KW - Natural language processing (Computer science). KW - Computational linguistics. KW - Artificial intelligence. KW - Data mining. KW - Natural Language Processing (NLP). KW - Computational Linguistics. KW - Artificial Intelligence. KW - Data Mining and Knowledge Discovery. KW - Algorithmic knowledge discovery KW - Factual data analysis KW - KDD (Information retrieval) KW - Knowledge discovery in data KW - Knowledge discovery in databases KW - Mining, Data KW - Database searching KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Automatic language processing KW - Language and languages KW - Language data processing KW - Linguistics KW - Natural language processing (Linguistics) KW - Applied linguistics KW - Cross-language information retrieval KW - Mathematical linguistics KW - Multilingual computing KW - NLP (Computer science) KW - Artificial intelligence KW - Human-computer interaction KW - Semantic computing KW - Data processing UR - https://www.unicat.be/uniCat?func=search&query=sysid:119428521 AB - 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. ER -