I would like to use non-atomic data, as a feature for a prediction. Suppose I have a Table with these features:
- Column 1: Categorical - House - Column 2: Numerical - 23.22 - Column 3: A Vector - [ 12, 22, 32 ] - Column 4: A Tree - [ [ 2323, 2323 ],[2323, 2323] , [ Boolean, Categorical ] ] - Column 5: A List [ 122, Boolean ]
I would like to predict/classify, for instance, Column 2.
I am making something to automatically respond to questions, any type of question, like "Where was Foo Born?" ...
I first make a query to a search engine, then I get some text data as a result, then I do all the parsing stuff (tagging, stemming, parsing, splitting ... )
My first approach was to make a table, each row with a line of text and a lot of features, like "First Word", "Tag of First Word", "Chunks", etc...
But with this approach I am missing the relationships between the sentences.
I would like to know if there is an algorithm that looks inside the tree structures (or vectors) and makes the relations and extract whatever is relevant for predicting/classifying. I'd prefer to know about a library that does that than an algorithm that I have to implement.