It's a great question and many of them are answered throughout the "core" chapters as well as in chapter 9, which discusses new and upcoming text applications.
Here are a few examples of things that can be built using the concepts in the book:
1. Sentiment analysis -- is this text positive or negative about a product/person/idea
2. Trend detection -- identifying what is trending in the news or in social media
3. Recommendation engine -- i.e. people who bought this also bought that.
4. Automatically identifying and extracting people, places, etc. from text
5. Classifying news into buckets like politics, sports, etc.
There are of course many others. At the end of the day, many of these techniques, esp. search, give you a real fast ranking engine, so any problem that needs ranking of top X items is a good candidate for search. Clustering, classification, named entity recognition are really good at helping you better organize unstructured content. They are also quite helpful in applications that have some component of text but aren't purely text based, like customer profile segmentation, etc.
Joined: Oct 07, 2005
Cool. Okay, that's genuinely interesting. I hadn't really thought of those applications.
Thank you for taking the time to answer.
subject: Taming Text: some more examples of where it's useful?