Product Description
by Teresa Jade (Author), Biljana Belamaric-Wilsey (Author), Michael Wallis (Author)
Extract actionable insights from text and unstructured data.
Information extraction is the task of automatically extracting structured information from unstructured or semi-structured text. SAS(R) Text Analytics for Business Applications: Concept Rules for Information Extraction Models focuses on this key element of natural language processing (NLP) and provides real-world guidance on the effective application of text analytics.
Using scenarios and data based on business cases across many different domains and industries, the book includes many helpful tips and best practices from SAS text analytics experts to ensure fast, valuable insight from your textual data.
Written for a broad audience of beginning, intermediate, and advanced users of SAS text analytics products, including SAS(R) Visual Text Analytics, SAS(R) Contextual Analysis, and SAS(R) Enterprise Content Categorization, this book provides a solid technical reference. You will learn the SAS information extraction toolkit, broaden your knowledge of rule-based methods, and answer new business questions. As your practical experience grows, this book will serve as a reference to deepen your expertise.
Author Biography
Teresa Jade, MA, is a principal linguistic specialist in Artificial Intelligence and Machine Learning, Research and Development, at SAS. She holds multiple master's degrees in linguistics. She loves big (text) data and analytics, and she has worked in the field of NLP for 19 years. Teresa started her career by working in Silicon Valley start-up companies for 9 years, and she has been at SAS for the past 6 years. She holds one NLP patent in categorization and information retrieval and has two pending NLP patent applications in information extraction and clause detection.
