Generating word clusters by graph clustering based on hearst patterns

The process of clustering similar words is crucial for a broad range of applications such as text classification and word sense disambiguation. Several approaches for deriving word similarity have been proposed. Some, like latent semantic analysis, are derived from the distributional hypothesis. Others extract relationships between terms by drawing upon predefined linguistic patterns. In this work, we propose an innovative approach which combines the essence of both these approaches. In the first phase, our algorithm generates a graphical model of terms and their interrelations with the help of special lexico-syntactic patterns called Hearst Patterns. We then apply a graph clustering technique to find semantically related words.