One of the main challenges in question-answering is the potential mismatch between the expressions in questions and the expressions in texts. While humans appear to use inference rules such as "X writes Y" implies "X is the author of Y" in answering questions, such rules are generally unavailable to question-answering systems due to the inherent difficulty in constructing them. In this paper, we present an unsupervised algorithm for discovering inference rules from text. Our algorithm is based on an extended version of Harris' Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar. Instead of using this hypothesis on words, we apply it to paths in the dependency trees of a parsed corpus. Essentially, if two paths tend to link the same set of words, we hypothesize that their meanings are similar. We use examples to show that our system discovers many inference rules easily missed by humans.
REF learned paraphrases from a parsed monolingual corpus based on an extended distributional hypothesis, where if two paths in dependency trees tend to occur in similar contexts it is hypothesized that the meanings of the paths are similar.
Discovery of Inference Rules for Question Answering
Natural Language Engineering
Computer Science