Rhetorical Figure Detection: the Case of Chiasmus

Abstract

We propose an approach to detecting the rhetorical figure called chiasmus, which involves the repetition of a pair of words in reverse order, as in “all for one, one for all”. Although repetitions of words are common in natural language, true instances of chiasmus are rare, and the question is therefore whether a computer can effectively distinguish a chiasmus from a random criss-cross pattern. We argue that chiasmus should be treated as a graded phenomenon, which leads to the design of an engine that extracts all criss-cross patterns and ranks them on a scale from prototypical chiasmi to less and less likely instances. Using an evaluation inspired by information retrieval, we demonstrate that our system achieves an average precision of 61%. As a by-product of the evaluation we also construct the first annotated corpus of chiasmi.