Towards context sensitive information inference

Humans can make hasty, but generally robust judgements about what a text fragment is, or is not, about. Such judgements are termed information inference. By drawing on theories from non-classical logic and applied cognition, an information inference mechanism is proposed which makes inferences via computations of information flow through a high dimensional conceptual space. Within a conceptual space information is represented geometrically. In this article, an approximation of a conceptual space is employed whereby geometric representations of words are realized as vectors in a high dimensional semantic space, which is automatically constructed from a text corpus. Two approaches were presented for priming vector representations according to context. The first approach uses a concept combination heuristic to adjust the vector representation of a concept in the light of the representation of another concept. The second approach computes a prototypical concept on the basis of exemplar trace texts and moves it in the dimensional space according to the context. Information inference is evaluated by measuring the effectiveness of query models derived by information flow computations. Results show that information flow contributes significantly to query model effectiveness, particularly with respect to precision. Moreover, retrieval effectiveness compares favourably with two probabilistic query models, and another based on semantic association. More generally, this article can be seen as a contribution towards realizing operational systems which mimic human text-based reasoning.