Automatic Tagging of Modality: identifying triggers and modal values

Abstract

We present an experiment in the automatic tagging of modality in Portuguese. As we are currently lacking a suitable resource with detailed modal information for Portuguese, we experiment with small sample of 160.000 tokens, manually annotated according to the modality scheme that we previously developed for European Portuguese (Hendrickx et al., 2012). We consider modality as the expression of the speaker (or subject)’s attitude towards the proposition and our modality scheme accounts for seven major modal values, and nine sub values. This experiment focuses on three modal verbs, poder ‘may/can’, dever ‘shall/might’ and conseguir ‘manage to/ succeed in/ be able to’, which may all have more than one modal value. We first report on the task of correctly detecting the modal uses of poder and dever, since these two verbs may have non modal meanings. For the identification of the modal value of each occurrence of those three verbs, we applied a machine learning approach that takes into consideration all the features available from a syntactic parser’s output. We obtained the best performance using SVM with a string kernel and the system improved the baseline for all three verbs, with a maximum F-score of 76.2.