Using Statistical Word Associations for the Retrieval of Strongly-Textual Cases

Lexical relationships allow a textual CBR system to establish case similarity beyond the exact correspondence of words. In this paper, we explore statistical models to insert associations between problems and solutions in the retrieval process. We study two types of models: word cooccurrences and translation alignments. These approaches offer the potential to capture relationships arising between a problem description and its corresponding textual solution. We present some experimental results and evaluate these with respect to a tf*idf approach.