An Open Domain Factoid QA Framework with Improved Validation Techniques

The generic Question Answering (QA) framework processes questions by querying a knowledge base and extracting answers from retrieved passages using various Natural Language Processing techniques. The problem is validating whether the retrieved passages from the passage retrieval module contain expected answers to asked questions. Besides, extraction based on lexical and syntactic similarities alone is not enough coverage for scoring the correct answers in a QA framework. Therefore, this work aims to infuse validation techniques into the QA framework. Four similarity scores (Word Form (WF), Word Order (WO), Distance (DIST), and Semantic Similarity (SemSim)) were implemented for Answer Extraction. Instant snippets returned by the Google search engine were used as a corpus to generate candidate answer sets. On a dataset of 1370 factoid questions, the proposed method achieved an accuracy of 77.71%, precision of 77.91%, recall of 91.37%, and F1-measure of 91.37%. The results show that the inclusion of the validation techniques helps reduce the time spent by the system in analyzing passages without possible answers. The proposed system could be adapted for automatic QA Systems and grading factoid computer-based tests.