Combining Wordnet and Morphosyntactic Information in Terminology Clustering

The paper presents results of clustering terms extracted from economic articles in Polish Wikipedia. First, we describe the method of automatic term extraction supported by linguistic knowledge. Then, we define different types of term similarities used in the clustering experiment. Term similarities are based on Polish Wordnet and morphosyntactic analysis of data. The latter takes into account: term contexts, coordinated sequences of terms, syntactic patterns in which terms appear and words that are parts of terms (such as their heads and modifiers). Then we performed several experiments with hierarchical clustering of the 400 most frequent terms. We present the results of clustering when different groups of similarity coefficients are applied. Finally, we present an evaluation that compares the results with manually obtained groups. Our results prove that morphosyntactic information can help or even serve themselves for initial clustering of terms in semantically coherent groups.