MITRE: description of the Alembic system used for MUC-6
MUC6 '95 Proceedings of the 6th conference on Message understanding
Using semantic templates for a natural language interface to the CINDI virtual library
Data & Knowledge Engineering - Special issue: Natural language and database and information systems: NLDB 03
Interlingual Information Extraction as a Solution for Multilingual QA Systems
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Answer formulation for question-answering
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
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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.