Corpus Based Method of Transforming Nominalized Phrases into Clauses for Text Mining Application

Nominalization is a linguistic phenomenon in which events usually described in terms of clauses are expressed in the form of noun phrases. Extracting event structures is an important task in text mining applications. To achieve this goal, clauses are parsed and the argument structure of main verbs are extracted from the parsed results. This kind of preprocessing has been commonly done in the past research. In order to extract event structure from nominalized phrases as well, we need to establish a technique to transform nominalized phrases into clauses. In this paper, we propose a method to transform nominalized phrases into clauses by using corpus-based approach. The proposed method first enumerates possible predicate/argument structures by referring to a nominalized phrase (noun phrase) and makes their ranking based on the frequency of each argument in the corpus. The algorithm based on this method was evaluated using a corpus consisting of 24,626 aviation safety reports in English and it achieved a 78% accuracy in transformation. The algorithm was also evaluated by applying a text mining application to extract events and their cause-effect relations from the texts. This application produced an improvement in the text mining application’s performance. key words: nominalization, predicate/argument structure, text mining, corpus based method