Desiderata For Tagging With WordNet Synsets Or MCCA Categories

Minnesota Contextual Content Analysis (MCCA) is a technique for characterizing the concepts and themes occurring in text (sentences, paragraphs, interview transcripts, books). MCCA tags each word with a category and examines the distribution of categories against norms representing general usage of categories. MCCA also scores texts in terms of social contexts that are similar to different functions of language. Distributions can be analyzed using non-agglomerative clustering to characterize the concepts and themes. MCCA categories have been mapped to WordNet senses. The &fining characteristics that emerge from the mapping and the statistical techniques used in MCCA for analyzing concepts and themes suggest that tagging with WordNet synsets or MCCA categories may produce epiphenomenal results that are misleading. We suggest that WordNet synsets and MCCA categories be augmented with further lexical semantic information for use after text is tagged or categorized. We suggest that such information is useful not only for the primary purposes of disambiguation in parsing and text classification in content analysis and information retrieval, but also for tasks in corpus analysis, discourse analysis, and automatic text summarization.