We present a novel approach for jointly disambiguating and clustering known and unknown concepts and entities with Markov Logic. Concept and entity disambiguation is the task of identifying the correct concept or entity in a knowledge base for a single-or multi-word noun (mention) given its context. Concept and entity clustering is the task of clustering mentions so that all mentions in one cluster refer to the same concept or entity. The proposed model (1) is global, i.e. a group of mentions in a text is disambiguated in one single step combining various global and local features, and (2) performs disambiguation, unknown concept and entity detection and clustering jointly. The disambiguation is performed with respect to Wikipedia. The model is trained once on Wikipedia articles and then applied to and evaluated on different data sets originating from news papers, audio transcripts and internet sources.
We build upon our previous Markov Logic based approach for joint concept disambiguation and clustering REF .
Jointly Disambiguating and Clustering Concepts and Entities with Markov Logic
COLING
Computer Science