A Semantic Approach for Question Classification Using Register Linear Based Model

Question classification module of a Question Answering System plays a very important role in identifying and providing results according to the user expectations. There are different types of methods involved during classification that can be applied to all kinds of domain like machine learning or using lexical database with its own advantages and disadvantages. Identifying the relevant approach for question classification for a specific domain is one of the foremost tasks. A study on different levels of questions including Blooms taxonomy and Costa taxonomy made our work to focus more on different categories of questions. To overcome these issues, we employ a question classifier using Register Linear (RL) models for a specific domain. The Register Linear (RL) Classification Model classifies the complex questions in a linear manner where each input is assigned to only one class. The RL classification model identifies the role of semantic provided in the input space which is divided into decision regions with the decision surfaces to be of linear functions of input x (sentence) for different set of classes. Initially, the Register Linear model identifies the role of semantics in a sentence and with these roles being identified, statistical relations between the concepts in the sentence are derived that produces a probability distribution over different set of classes. With these classifications, the exact answer type is identified that helps to find the answer. Our model gives better results in terms of execution time (time taken to categorize the queries), classification accuracy and result analyzing efficiency.