Learning argument/adjunct distinction for Basque

Laburpena

This paper presents experiments performed on lexical knowledge acquisition in the form
of verbal argumental information. The system obtains the data from raw corpora after the
application of a partial parser and statistical filters. We used two different statistical filters to acquire the argumental information: Mutual Information, 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 developed for other languages.
   This work addresses the problem of learning subcategorization frames by distinguishing
arguments from adjuncts, being the last ones the most significant source of noise in
subcategorization frame acquisition.