3. What are the innovative characteristics of Deep a architecture that made watson superior The DeepQA architecture involves massive parallelism, many experts, pervasive confidence estimation, and integration of the-latest-and-greatest in-text analytics, involving both shallow and deep semantic knowledge. As implemented in Watson, DeepQa brings more than 100 different techniques for analyzing natural language identifying sources, finding anking ypotheses. More important than any nd generating hypotheses, find ing and scoring particular technique is the combination of overlapping approaches that can bring their strengths to bear and contribute to improvements in accuracy, confidence, and Why did I BM spend all that time and money to build Watson? Where is the rol? IBMs goal was to advance computer science by exploring new ways for computer technology to affect science, business, and society. The techniques IBM developed with DeepQA and Watson are relevant in a wide variety of domains central to IBMs mission. For example, IBM is currently working on a version of Watson to take on surmountable problems in healthcare and medicine. If successful, this could give IBM a distinct competitive advantage in this important technological application ar Section 5.2 Review Questions What is text analytics? How does it differ from text mining? Text analytics is a concept that includes information retrieval (e. g, searching and identifying relevant documents for a given set of key terms)as well as information extraction, data mining, and Web mining. By contrast, text mining is primarily focused on discovering new and useful knowledge from textual data sources. The overarching goal for both text analytics and text mining is to turn unstructured textual data into actionable information through the application of natural language processing(NLP)and analytics. However, text analytics is a broader term because of its inclusion of information retrieval. you can think of text analytics as a combination of information retrieval plus text mining 2. What is text mining? How does it differ from data mining? Text mining is the application of data mining to unstructured, or less structured text files. As the names indicate, text mining analyzes words, and data mini alyzes numeric data Copyright C2018 Pearson Education, Inc.3 Copyright © 2018Pearson Education, Inc. 3. What are the innovative characteristics of DeepQA architecture that made Watson superior? The DeepQA architecture involves massive parallelism, many experts, pervasive confidence estimation, and integration of the-latest-and-greatest in-text analytics, involving both shallow and deep semantic knowledge. As implemented in Watson, DeepQA brings more than 100 different techniques for analyzing natural language, identifying sources, finding and generating hypotheses, finding and scoring evidence, and merging and ranking hypotheses. More important than any particular technique is the combination of overlapping approaches that can bring their strengths to bear and contribute to improvements in accuracy, confidence, and speed. 4. Why did IBM spend all that time and money to build Watson? Where is the ROI? IBM’s goal was to advance computer science by exploring new ways for computer technology to affect science, business, and society. The techniques IBM developed with DeepQA and Watson are relevant in a wide variety of domains central to IBM’s mission. For example, IBM is currently working on a version of Watson to take on surmountable problems in healthcare and medicine. If successful, this could give IBM a distinct competitive advantage in this important technological application area. Section 5.2 Review Questions 1. What is text analytics? How does it differ from text mining? Text analytics is a concept that includes information retrieval (e.g., searching and identifying relevant documents for a given set of key terms) as well as information extraction, data mining, and Web mining. By contrast, text mining is primarily focused on discovering new and useful knowledge from textual data sources. The overarching goal for both text analytics and text mining is to turn unstructured textual data into actionable information through the application of natural language processing (NLP) and analytics. However, text analytics is a broader term because of its inclusion of information retrieval. You can think of text analytics as a combination of information retrieval plus text mining. 2. What is text mining? How does it differ from data mining? Text mining is the application of data mining to unstructured, or less structured, text files. As the names indicate, text mining analyzes words; and data mining analyzes numeric data