Here we will show how several interacting features may be conjoined in a single loglinear statistical model. We will focus on the problem of verbal transitivity, since it was there that we found the need for the introduction of more features: the main problem was the exchange of the traditional Subject-Verb-Object order that occurs in some Portuguese verbs. After a brief description of the framework on learning subcategorization by using loglinear models, we will present a comparative study on three possible feature sets. Several loglinear models with and without interactions among features and scores will be used. The results of the clustering process will be evaluated based on the independent classification presented in commercial dictionaries. Improvements where particularly noticeable in intransitive verbs where the precision raised from 82% to 91%. Conclusions will be drawn regarding the advantages and problems of introducing more features in a particular loglinear model.
The acquired results are presently paving the way to newer parsing mechanisms, capable of automatically overcome lack of subcategorization in lexica.