Implicit discourse relation recognition is a challenging task in the natural language processing field, but important to many applications such as qu estion answering, summarizat ion and so on. Previous research used either art ificially created imp licit discourse relat ions with connectives removed fro m exp licit relations or annotated implicit relat ions as training data to detect the possible implicit relations, and do not further discern which examples are fit to be training data. This paper is the first time to apply a d ifferent typical/atypical perspective to select the most suitable discourse relation examp les as training data. To differentiate typical and atypical examples for each discourse relation, a novel single centroid clustering algorithm is proposed. With this typical/atypical distinction, we aim to recognize those easily identified discourse relations more p recisely so as to promote the performance of the implicit relation recognition. The experimental results verify that the proposed new method outperforms the state -of-the-art methods.
REF proposed to differentiate typical and atypical examples for each discourse relation, and augment training data for implicits only by typical explicits.
Implicit Discourse Relation Recognition by Selecting Typical Training Examples
COLING
Computer Science