Statistics of Natural Language Processing Basics

Author: (American) Manning (Manning/C.D.) et al. / Yuan Chunfa et al.
Publisher:
Publishing Date: 2005-01-01
Features: In recent years, statistical natural language processing (or statistical linguistics) has emerged as a rising star and has become the mainstream in natural language processing research. During the growth of the field of statistical natural language processing, four factors have played a driving role: 1. Due to the development of computer hardware, large-capacity storage and high-speed computing have become possible; 2. Due to the widespread use of computer networks, the emergence of a large amount of electronic text online has made corpus acquisition no longer difficult; 3. The development of the field of machine learning has become increasingly mature and has been widely applied in many areas, making its application in natural language processing a natural thing; 4. Due to the inherent complexity of natural language, even linguists find it difficult to describe it using purely artificial rules (or patterns), which forces us to learn language patterns from actual corpora. The research on statistical natural language processing involves all aspects of traditional natural language processing, such as language analysis, machine translation, information retrieval, and text classification. It can be said without exaggeration that the introduction of statistical learning methods has greatly promoted the research and development of these fields. Currently, almost all renowned universities in China have computer departments engaged in research in this field (or have offered similar majors). However, systematic teaching or reading of monographs on this topic has not been given enough attention by academic peers. At an academic conference, a professor from a university said with deep feeling, "Graduate students must read a monograph seriously during their studies." We deeply resonate with this professor's statement. Graduate students must read recent references, including conference papers and journal articles, but if they only read these materials without (or learning) one or two monographs, the knowledge they acquire may be fragmented and may give the impression of being overlyistic, especially for emerging disciplines. Research conducted under such circumstances often lacks confidence and finds it difficult to produce outstanding results. In academic exchanges, people often lack a common language, even leading to humorous misunderstandings. This book is a systematic monograph introducing statistical natural language processing (or statistical linguistics), which has been used as a textbook by many universities abroad. In China, people have begun to recognize the value of this book, and many universities have adopted the English version of this book as a graduate textbook. Translating and introducing this monograph to a wide range of readers in China engaged in natural language processing research has significant practical importance. This book covers the most important topics in various fields of statistical natural language processing, with detailed content and clear organization. Whether for researchers working in information retrieval, machine translation, text classification, and language analysis, or for undergraduate and graduate students in computational linguistics, this book is of great reference value. This book was organized and translated by Yuan Chunfa of the Computer Science Department of Tsinghua University. Yuan Chunfa has long been engaged in research and teaching in the field of statistical natural language processing and has a deep understanding of the issues in this field. The co-translators also have a certain research foundation and experience in this field. Chapters 2 and 13–16 were initially translated by Li Qingzhong, Chapters 1 and 5–8 by Wang Yun, Chapters 3 and 9–12 by Li Wei, and the preface and Chapter 4 by Cao Defang. Finally, Yuan Chunfa was responsible for unifying the revisions, reviews, and finalization of the entire book. During the translation process of this book, everyone strived to be faithful to the original text while ensuring that the concepts were expressed accurately and clearly. Professor Huang Changning provided guidance on the translation work, and people like Wen Yang, Zhou Jianhui, Xu Wei, Weng Yao, Qian Donglei, and Lin Jing also contributed to the translation and auxiliary work. Gratitude is expressed to all of them here. This book was translated based on the fifth printing of the English version, and the relevant content has been corrected or annotated according to the errata list provided by the author on the website. Due to the limited capabilities of the translators, some inaccuracies may still exist in the translation. We hope that the readers will point out any issues. In recent years, statistical methods in natural language processing have gradually become mainstream. This book is a comprehensive and systematic monograph introducing statistical natural language processing techniques, selected as a textbook for computational linguistics-related courses by many renowned universities at home and abroad. The content of this book is very extensive, divided into four parts and 16 chapters, covering almost all theories and algorithms needed to build natural language processing software tools. The entire book progresses from, from mathematical foundations to precise theoretical algorithms, from simple lexical analysis to complex syntactic analysis, catering to the needs of readers of different levels. At the same time, this book closely links theory and practice, providing high-level applications of natural language processing techniques (such as information retrieval) on the basis of introducing theoretical knowledge. Many resources and tools are provided on the accompanying website of this book to help readers improve their skills through practical exercises based on the book. This book is not only suitable as a textbook for graduate students in the field of natural language processing but is also an excellent reference for researchers and technicians in related fields.