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The Further Emergence of Natural Language Processing

by William Laurent, William Laurent, Inc.Monday, December 27, 2010

Nobody knows for sure where Web 2.0 leaves off and where Web 3.0 begins, or when the Web 3.0 will officially “arrive”. Despite a few years of prognostication there remains no widespread consensus on what Web 3.0 really is. In the past I have encountered a substantial amount of healthy disagreement over my definition of Web 3.0.; so rather than try to pontificate on what exactly Web 3.0 is or what it means, I prefer to talk about how we will get there.

The world will enter the era of Web 3.0 when the Internet sees vastly improved methods of classifying data and searching for that data. In some aspects we have already arrived at Web 3.0, thanks to the blossoming of intelligent new data taxonomies and classification technologies, as well as deep improvements on how this data is stored, retrieved, and discovered. But there is still much progress to be made in bridging the gap between linguistics and computer technology so that the World Wide Web can ascribe useful meanings to human language and text. This is where NLP (Natural Language Processing) enters the picture.

NLP is positioned to be a key enabler of Web 3.0 because of its ability to deeply parse and make sense of textual knowledge. NLP goes far beyond the usual “shallow parsing” of most search engine technologies by using text analytics to unlock the hidden contexts and subtleties from the Web. NLP functions as a quasi-artificial intelligence, translating human language and mapping that language as symbologies, which it then classifies into predefined information taxonomies.

When data mining leverages NLP very complex patterns and trends (such as purchasing or Internet search behaviors on a macro level) can be inferred and elucidated. With NLP, undiscovered behavioral patterns can be deduced from discussions on the most popular shopping and social media sites (such as a discussion forum on Craig’s List or Amazon.com), so that consumer or community sentiment may be more fully and holistically understood. Nascent trends can be spotted by mining customer reviews or likes/dislikes and applying segmentation and cluster analysis. This type of information forms the basis of descriptive modeling, which is often an antecedent to predictive modeling (the same data can be fed into predictive models so that business can build better products and more intelligently place them in the marketplace).

NLP currently plays a huge part in making search engine results more useful to the browsing public. The combination of NLP, BI, and data mining has largely contributed to the continued success of Google’s search engine. Thanks in part to NLP, Google has designed a number of very nifty (and highly proprietary) algorithms that enable it to serve up very targeted and customized advertising and search results. For the end user the  search experience is much improved; and for Google there is a very nice revenue stream being generated every day. The more relevant an advertisement is to the Internet user conducting the web search, the more likely it is that they will click on the advertisement and ultimately purchase the product or service advertised. It is absolutely remarkable how relevant and user-targeted Google’s advertisements have gotten, and how much Google’s revenue has increased accordingly. Google leads all competing search engines (by a large margin) in utilizing NLP algorithms to interpret text strings and make millisecond determinations as to what a consumer may really be searching for, thereby serving up the most appropriate and germane links on the World Wide Web. Moreover, Google’s search engine checks for any misspellings in the user’s query and suggests pertinent findings via its “Did you Mean” result set.

Truth be told, I do have concerns about my personal privacy as it relates to using Google’s search engine. Google stores the information from every search for at least 18 months, associating searches with a user’s account or their particular computer/IP address. Data mining techniques are then used to hone in on a user’s shopping habits and personal interests. While I understand that they are using my search engine queries to better market goods and services to me, I can’t help but be concerned about the Big Brother aspect of this development. There are some pretty scary implications as to who may have access to my web surfing history in the future. Meanwhile, Google continues to improve their NLP algorithms and increase the breadth and depth of information about user searches in their databases. Web 3.0 may not just be sunshine and roses; it may hold some ominous developments. One thing is for sure: the future of the Internet will only get more and more interesting!

About the Author

William Laurent is one of the world's leading experts in information strategy and governance. For 20 years, he has advised numerous businesses and governments on technology strategy, performance management, and best practices—across all market sectors. William currently runs an independent consulting company that bears his name. In addition, he frequently teaches classes, publishes books and magazine articles, and lectures on various technology and business topics worldwide. As Senior Contributing Author for Dashboard Insight, he would enjoy your comments at wlaurent@williamlaurent.com

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