Automating the encoding for the LISA model of analogy from raw text

Analogy is an integral part of human cognition. Consequently researchers would like to produce computational models of analogy to test implementations of theoretical claims. Such computational models have relied almost exclusively upon hand-coded representations, making the resulting models too dependent upon the modeler's choices, and thus difficult to interpret in isolation of the modeler. In an effort to combat this dependency, we present here a means of automatic encoding for the LISA model of analogy and inference.