Environment top DIGG news

A different way to search?

advertisement:

I recently had an interesting conversation about classification and search with James Zubok, CFO of Brainware, Inc. Brainware is a Virginia based company that was once part of SER Systems AG, a former German ECM company. Brainware provides products that help companies extract information from unstructured and semi-structured documents, such as invoices, order forms, contracts, etc. without using templates. The company also offers some interesting classification and search technology and this is what our conversation focused on.

We discussed two different, but interrelated, technologies that Brainware has developed; one a search engine based on n-grams and another, a classification engine that uses neural networks. Brainware offers both enterprise and desktop editions of each. I received a demo of the desktop version of the products and now have both running on my laptop.

A Search example On the desktop search side, the product, called Globalbrain Personal Edition, differs from many other search products on the market in that it does not make use of keyword search. Rather, its searches are natural language based, using a patented n-gram approach. When indexing a word, the word is parsed into three parts and then a vector is created. For example, the word sample would be parsed as sam, amp, mpl, etc. According to Brainware, this three-letter snippet approach makes the search engine language independent. The capability provided by Brainware lets users search, not simply on key words, but on whole paragraphs. For example, I have many documents (in various formats) on my desktop that deal with all of the companies I speak with. Say, I want to find some documents relating to specific challenges companies faced in deploying their text analytics solutions. Rather than simply inputting “text analytics” and “challenges”, I can type in a phrase or even a paragraph with the wording I’m looking for. This returns a much more targeted set of documents.

A Classification example On the desktop classification front, the product is very easy to use. I simply loaded the software which provided me a user interface where I could develop classes and then train my system to automatically classify documents based on a few training examples. As I mentioned, I have many documents on my desktop that deal with various technology areas and I might want to classify them in an intelligent manner for some research I’m planning. So, I created several classes: text analytics, visualization, and MDM. I simply created these classes and then dragged documents that I felt fell into each category onto those classes. I trained the system on these examples.

Brainware provides a visual interface that lets me view how “good” the learned set is via a series of points in three-dimensional space. The closer together the points (from the same class) are on the plot, the better the classification will be. Also, the more separate the various class points are, the better the classification. In my classification test, the visualization and the MDM documents were tightly clustered, while the text analytics information was not. In any event, I then ran the classifier over the rest of my documents (supplying a few parameters) and the system automatically classified what it cou

Nfl draft picks

Related posts: Stx, Monochrome factor, One and only palmilla resort, Daisy de la hoya naked, Federici

Filed under: Uncategorized