Distributional Framework for Emergent Knowledge Acquisition and its Application to Automated Document Annotation
This addresses the challenge of automated document annotation in life sciences, but appears incremental as it builds on existing distributional and tensor methods.
The paper tackles the problem of unsupervised knowledge acquisition from text by introducing a tensor-based distributional framework to infer emergent patterns like term relationships and IF-THEN rules, and applies it to annotate biomedical articles with MeSH terms.
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).