In this paper, we present a comparative analysis of statistically predictive syntactic features of complexity and the treatment of these features by humans when simplifying texts. To that end, we have used a list of the most five statistically predictive features obtained automatically and the Corpus of Basque Simplified Texts (CBST) to analyse how the syntactic phenomena in these features have been manually simplified. Our aim is to go beyond the descriptions of operations found in the corpus and relate the multidisciplinary findings to understand text complexity from different points of view. We also present some issues that can be important when analysing linguistic complexity.

This paper presents the work that has been carried out to annotate semantic roles in the Basque Dependency Treebank (BDT). We will describe the resources we have used and the way the annotation of 100 verbs has been done. We decide to follow the model proposed in the PropBank project that has been deployed in other languages, such as Chinese, Spanish, Catalan and Russian. The resources used are: an in-house database with syntactic/semantic subcategorization frames for Basque verbs, an English-Basque verb mapping based on Levin’s classification and the BDT itself. Detailed guidelines for human taggers have been established as a result of this annotation process. In addition, we have characterized the information associated to the semantic tag. Besides, and based on this study, we will define semi-automatic procedures that will facilitate the task of manual annotation for the rest of the verbs of the Treebank. We have also adapted AbarHitz, a tool used in the construction of the BDT, for the task of annotating semantic roles according to the proposed characterization.