Distributional Framework for Emergent Knowledge Acquisition and its Application to Automated Document Annotation

The paper introduces a framework for representation and acquisition of knowledge emerging from large samples of textual data. We utilise a tensor-based, dis-tributional representation of simple statements extracted from text, and show how one can use the representation to infer emergent knowledge patterns from the tex-tual data in an unsupervised manner. Examples of the patterns we investigate in the paper are implicit term relationships or conjunctive IF-THEN rules. To evaluate the practical relevance of our approach, we apply it to annotation of life science articles with terms from MeSH (a controlled biomedical vocabulary and thesaurus).