Broad domain question answering is often difficult in the absence of structured knowledge bases, and can benefit from shallow lexical methods (broad coverage) and logical reasoning (high precision). We propose an approach for incorporating both of these signals in a unified framework based on natural logic. We extend the breadth of inferences afforded by natural logic to include relational entailment (e.g., buy → own) and meronymy (e.g., a person born in a city is born the city's country). Furthermore, we train an evaluation function - akin to gameplaying -to evaluate the expected truth of candidate premises on the fly. We evaluate our approach on answering multiple choice science questions, achieving strong results on the dataset.
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