Computational Methods for Corpus Annotation and Analysis, Softcover reprint of the original 1st ed. 2014

In the past few decades the use of increasingly large text corpora has grown rapidly in language and linguistics research. This was enabled by remarkable strides in natural language processing (NLP) technology, technology that enables computers to automatically and efficiently process, annotate and analyze large amounts of spoken and written text in linguistically and/or pragmatically meaningful ways. It has become more desirable than ever before for language and linguistics researchers who use corpora in their research to gain an adequate understanding of the relevant NLP technology to take full advantage of its capabilities.
This volume provides language and linguistics researchers with an accessible introduction to the state-of-the-art NLP technology that facilitates automatic annotation and analysis of large text corpora at both shallow and deep linguistic levels. The book covers a wide range of computational tools for lexical, syntactic, semantic, pragmatic and discourse analysis, together with detailed instructions on how to obtain, install and use each tool in different operating systems and platforms. The book illustrates how NLP technology has been applied in recent corpus-based language studies and suggests effective ways to better integrate such technology in future corpus linguistics research.
This book provides language and linguistics researchers with a valuable reference for corpus annotation and analysis.

Provides an accessible introduction to natural language processing technology that facilitates automatic corpus annotation and analysis Covers computational tools for lexical, syntactic, semantic, pragmatic and discourse analysis Offers detailed instructions on how to obtain, install and use each tool Illustrates how NLP technology has been applied in recent corpus-based language studies Suggests effective ways to integrate NLP technology in future applied corpus linguistic research Includes supplementary material: sn.pub/extras