  Question Answering (QA) research is a significant and challenging task in
Natural Language Processing. QA aims to extract an exact answer from a relevant
text snippet or a document. The motivation behind QA research is the need of
user who is using state-of-the-art search engines. The user expects an exact
answer rather than a list of documents that probably contain the answer. In
this paper, for a successful answer extraction from relevant documents several
efficient features and relations are required to extract. The features include
various lexical, syntactic, semantic and structural features. The proposed
structural features are extracted from the dependency features of the question
and supported document. Experimental results show that structural features
improve the accuracy of answer extraction when combined with the basic features
and designed using dependency principles. Proposed structural features use new
design principles which extract the long-distance relations. This addition is a
possible reason behind the improvement in overall answer extraction accuracy.
