ClusType

Abstract

Entity recognition is an important but challenging research problem. In reality, many text collections are from specific, dynamic, or emerging domains, which poses significant new challenges for entity recognition with increase in name ambiguity and context sparsity, requiring entity detection without domain restriction. In this paper, we investigate entity recognition (ER) with distant-supervision and propose a novel relation phrase-based ER framework, called <b>ClusType</b>, that runs <i>data-driven</i> phrase mining to generate entity mention candidates and relation phrases, and enforces the principle that relation phrases should be <i>softly</i> clustered when propagating type information between their argument entities. Then we predict the type of <i>each</i> entity mention based on the type signatures of its co-occurring relation phrases and the type indicators of its surface name, as computed over the corpus. Specifically, we formulate a joint optimization problem for two tasks, <i>type propagation with relation phrases</i> and <i>multi-view relation phrase clustering</i>. Our experiments on multiple genres-news, Yelp reviews and tweets-demonstrate the effectiveness and robustness of ClusType, with an average of 37% improvement in F1 score over the best compared method.