This cross-disciplinary book is meant for a wide range of readers in artificial intelligence, cognitive science, psychometrics, comparative psychology, and philosophy. It provides a unified framework for evaluating behavioral features of both natural and artificial intelligence, and critically analyzes what the future of intelligence may look like.
For undergraduate or advanced undergraduate courses in Classical Natural Language Processing, Statistical Natural Language Processing, Speech Recognition, Computational Linguistics, and Human Language Processing. An explosion of Web-based language techniques, merging of distinct fields, availability of phone-based dialogue systems, and much more make this an exciting time in speech and language processing. The first of its kind to thoroughly cover language technology - at all levels and with all modern technologies - this text takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. The authors cover areas that traditionally are taught in different courses, to describe a unified vision of speech and language processing. Emphasis is on practical applications and scientific evaluation. An accompanying Website contains teaching materials for instructors, with pointers to language processing resources on the Web. The Second Edition offers a significant amount of new and extended material. Supplements: Click on the Resources tab to View Downloadable Files: * Solutions * Power Point Lecture Slides - Chapters 1-5, 8-10, 12-13 and 24 Now Available! * For additional resourcse visit the author website:
This book is about a new approach in the field of computational linguistics related to the idea of constructing n-grams in non-linear manner, while the traditional approach consists in using the data from the surface structure of texts, i.e., the linear structure. In this book, we propose and systematize the concept of syntactic n-grams, which allows using syntactic information within the automatic text processing methods related to classification or clustering. It is a very interesting example of application of linguistic information in the automatic (computational) methods. Roughly speaking, the suggestion is to follow syntactic trees and construct n-grams based on paths in these trees. There are several types of non-linear n-grams; future work should determine, which types of n-grams are more useful in which natural language processing (NLP) tasks. This book is intended for specialists in the field of computational linguistics. However, we made an effort to explain in a clear manner how to use n-grams; we provide a large number of examples, and therefore we believe that the book is also useful for graduate students who already have some previous background in the field.
Now a days, enormous number of news articles are reported and disseminated on the web. Extracting important information from the news articles to reduce the reading time is the essential issue. Gist generation is an important, difficult and interesting Natural Language Processing (NLP) problem as it requires to mine the essential content words from an article and also to generate a gist that expresses the summary of an article. In ideal case, summary of an article need to generate directly from the understanding of an article. But, developing such type of NLP systems are not possible. This book introduces the importance of the Short Summary Generation. It presents the different preprocessing techniques for improving the performance of the methods. It discusses the various approaches for Content Word Selection from the article and compares the performance of the methods with various measures such as precision, recall and F-measures. The empirical evaluations are carried out for topic sentence identification. The Hybrid approach is presented for Short Summary generation using topic sentences.