Main object extraction

As I wrote earlier, the first step of answering a question is finding out what it's asking about. The question "How old do you have to be to play Monopoly?" is asking about the board game Monopoly. I've proposed a method to train a model to be able to do this using K-parser.

I'll illustrate my process with a more complex example: "how many schools are in the school district of philadelphia?" The KDG looks like this (for brevity, I replaced every element with its class and stripped unimportant nodes):

root: be
  agent: schools
    trait: ?
      subclass_of: quantity
  is_inside_location: district
     complement_word: school
     is_part_of: philadelphia

(Take note that the ? with the subclass "quantity" represents the words "How many...?")

I have an operation that takes a node of the graph and returns a list of strings. It builds a list of strings out of the node's name, and the names of any "complement_word", "trait", "is_part_of", etc. nodes that come out of it. For example, the strings for "district" would be {"district", "school", "philadelphia"}. I then can concatenate them ("district school philadelphia") and search for that using Google's Freebase API.

The trick here is finding the correct node to do this on. One node in the graph (and the subgraph that stems from it) usually represents the main object of the question. I think a model can be trained with (graph structure, path to correct node) pairs. These pairs can be extracted from the training set by taking each KDG and using brute force to find the correct path. It works on most of the sentences that K-parser correctly parses; my experiments right now give me 75% but I'm sure it can be improved to around 90%.

The crux of the idea is that the actual entities do not matter in finding the main entity, only the words between them. With a single training example: "how many schools are in the school district of philadelphia," the model can now extract the main object of any question of the form "how many X are in Y." With over 600 examples, this system should be able to generalize to all forms of questions. However, whether that is truly the case remains to be seen, because I haven't implemented this system yet.