How to extract relationships from a text - nlp

Question

I am currently new with NLP and need guidance as of how I can solve this problem.
I am currently doing a filtering technique where I need to brand data in a database as either being correct or incorrect. I am given a structured data set, with columns and rows.
However, the filtering conditions are given to me in a text file.
An example filtering text file could be the following:
Values in the column ID which are bigger than 99
Values in the column Cash which are smaller than 10000
Values in the column EndDate that are smaller than values in StartDate
Values in the column Name that contain numeric characters
Any value that follows those conditions should be branded as bad.
However, I want to extract those conditions and append them to the program that I've made so far.
For instance, for the conditions above, I would like to produce
`if ID>99`
`if Cash<10000`
`if EndDate < StartDate`
`if Name LIKE %[1-9]%`
How can I achieve the above result using the Stanford NLP? (or any other NLP library).

Answer 1

This doesn't look like a machine learning problem; it's a simple parser. You have a simple syntax, from which you can easily extract the salient features:
column name
relationship
target value or target column
The resulting "action rule" is simply removing the "syntactic sugar" words and converting the relationship -- and possibly the target value -- to its symbolic form.
Enumerate all of your critical words for each position in a lexicon. Then use basic string manipulation operators in your chosen implementation language to find the three needed fields.
EXAMPLE
Given the data above, your lexicons might be like this:
column_trigger = "Values in the column"
relation_dict = {
"are bigger than" : ">",
"are smaller than" : "<",
"contain" : "LIKE",
...
}
value_desc = {
"numeric characters" : "%[1-9]%",
...
}
From here, use these items in standard parsing. If you're not familiar with that, please look up the basics of a simple sentence grammar in your favourite programming language, with rules such as such as
SENTENCE => SUBJ VERB OBJ
Does that get you going?