WordNet-based lexical semantic classification for text corpus analysis

Many text classifications depend on statistical term measures to implement document representation. Such document representations ignore the lexical semantic contents of terms and the distilled mutual information, leading to text classification errors. This work proposed a document representation method, WordNet-based lexical semantic VSM, to solve the problem. Using WordNet, this method constructed a data structure of semantic-element information to characterize lexical semantic contents, and adjusted EM modeling to disambiguate word stems. Then, in the lexical-semantic space of corpus, lexical-semantic eigenvector of document representation was built by calculating the weight of each synset, and applied to a widely-recognized algorithm NWKNN. On text corpus Reuter-21578 and its adjusted version of lexical replacement, the experimental results show that the lexical-semantic eigenvector performs F1 measure and scales of dimension better than term-statistic eigenvector based on TF-IDF. Formation of document representation eigenvectors ensures the method a wide prospect of classification applications in text corpus analysis.