Contextual text representation for unsupervised knowledge discovery in texts

This paper studies the role of lexical contextual relations for the problem of unsupervised knowledge discovery in full texts. Narrative texts have inherent structure dictated by language usage in generating them. We suggest that the relative distance of terms within a text gives sufficient information about its structure and its relevant content. Furthermore, this structure can be used to discover implicit knowledge embedded in the text, therefore serving as a good candidate to represent effectively the text content for knowledge elicitation tasks. We qualitatively demonstrate that a useful text structure and content can be systematically extracted by collocational lexical analysis without the need to encode any supplemental sources of knowledge. We present an algorithm that systematically extracts the most relevant facts in the texts and labels them by their overall theme, dictated by local contextual information. It exploits domain independent lexical frequencies and mutual information measures to find the relevant contextual units in the texts. We report results from experiments in a real-world textual database of psychiatric evaluation reports.