This paper presents the experiments performed on lexical knowledge acquisition in the form of verbal subcategorization information. The system obtains the data from raw corpora after the application of a partial parser and statistical filters. We used three different statistical filters to acquire the subcategorization information: Mutual Information, Pearson’s Chi-square and Fisher’s Exact test. Due to the characteristics of agglutinative languages like Basque, the usual classification of arguments in terms of their syntactic category (such as NP or PP) is not suitable. For that reason, the arguments will be classified in 48 different kinds of case markers, which makes the system fine grained if compared to equivalent systems that have been developed for other languages. This work addresses the problem of distinguishing arguments from adjuncts, being this one of the most significant sources of noise in subcategorization frame acquisition.