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The paper introduces a framework for representation and acquisition ofknowledge emerging from large samples of textual data. We utilise atensor-based, distributional representation of simple statements extracted fromtext, and show how one can use the representation to infer emergent knowledgepatterns from the textual data in an unsupervised manner. Examples of thepatterns we investigate in the paper are implicit term relationships orconjunctive IF-THEN rules. To evaluate the practical relevance of our approach,we apply it to annotation of life science articles with terms from MeSH (acontrolled biomedical vocabulary and thesaurus).

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