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This paper proposes an approach using taxonomic relatedness for answer‐type recognition and type coercion in a question‐answering system. We introduce a question analysis method for a lexical answer type (LAT) and semantic answer type (SAT) and describe the construction of a taxonomy linking them. We also analyze the effectiveness of type coercion based on the taxonomic relatedness of both ATs. Compared with the rule‐based approach of IBM's Watson, our LAT detector, which combines rule‐based and machine‐learning approaches, achieves an 11.04% recall improvement without a sharp decline in precision. Our SAT classifier with a relatedness‐based validation method achieves a precision of 73.55%. For type coercion using the taxonomic relatedness between both ATs and answer candidates, we construct an answer‐type taxonomy that has a semantic relationship between the two ATs. In this paper, we introduce how to link heterogeneous lexical knowledge bases. We propose three strategies for type coercion based on the relatedness between the two ATs and answer candidates in this taxonomy. Finally, we demonstrate that this combination of individual type coercion creates a synergistic effect.

Agency for Defense DevelopmentThis paper include the results of the software interface design for the ground vehicle simulators. This design allows us to upgrade the simulator easily by exchanging the engineering model when we need to change the function of algorithm or data of a simulator. That is because a main part of simulator is composed of reusable model. †