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.