EAN: 9789819916023

REPRESENTATION LEARNING FOR NATURAL LANGUAGE PR...

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.

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