Answer Extraction in Question Answering using Structure Features and Dependency Principles

TL;DR

This paper introduces structural features based on dependency principles to enhance answer extraction accuracy in question answering systems, demonstrating that these features improve performance when combined with traditional features.

Contribution

The paper proposes novel structural features derived from dependency relations that capture long-distance dependencies, improving answer extraction accuracy in QA systems.

Findings

Structural features improve answer extraction accuracy.

Combining structural with lexical, syntactic, semantic features yields better results.

Dependency-based features effectively capture long-distance relations.

Abstract

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.