QUANTUM: A Function-Based Question Answering System

Abstract In this paper, we describe our Question Answering (QA) system called QUANTUM. The goal of QUANTUM is to find the answer to a natural language question in a large document collection. QUANTUM relies on computational linguistics as well as information retrieval techniques. The system analyzes questions using shallow parsing techniques and regular expressions, then selects the appropriate extraction function. This extraction function is then applied to one-paragraph-long passages retrieved by the Okapi information retrieval system. The extraction process involves the Alembic named entity tagger and the WordNet semantic network to identify and score candidate answers. We designed QUANTUM according to the TREC-X QA track requirements; therefore, we use the TREC-X data set and tools to evaluate the overall system and each of its components.

• Type: Journal
• Year: 2002
• Where: AI
• Authors: Luc Plamondon, Leila Kosseim
• Keywords: AI 2002, Artificial Intelligence, Extraction Function, Information Retrieval, Quantum