A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging

In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task. Our approach is based on an incremental knowledge acquisition method where rules are stored in an exception structure and new rules are only added to correct the errors of existing rules; thus allowing systematic control of the interaction between the rules. Experimental results on 13 languages show that our approach is fast in terms of training time and tagging speed. Furthermore, our approach obtains very competitive accuracy in comparison to state-of-the-art POS and morphological taggers.Comment: Version 1: 13 pages. Version 2: Submitted to AI Communications - the European Journal on Artificial Intelligence. Version 3: Resubmitted after major revisions. Version 4: Resubmitted after minor revisions. Version 5: to appear in AI Communications (accepted for publication on 3/12/2015

Latin 1st class -\u101- verbs as thematic formations: On the deficiency of IE roots

This study deals with the status of the morphological element -\u101 which marks 1st conjugation verbs in Latin. Adopting a Distributed Morphology framework, I focus on de-nominal/de-adjectival verbs and more generally on derivative ones, beside that on 'primary' -\u101 verbs which are the direct outcome of a PIE root. I demonstrate that -\u101 arises from the nominal domain, and that it is basically associated to agentive Voice. It covers the function of a thematic vowel in order to repair a marked matrix of features, due to the roots which lack an overt verbal character