  The paper introduces a framework for representation and acquisition of
knowledge emerging from large samples of textual data. We utilise a
tensor-based, distributional representation of simple statements extracted from
text, and show how one can use the representation to infer emergent knowledge
patterns from the textual 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).
