The Morpho-syntactic Annotation of Animacy for a Dependency Parser

In this paper we present the annotation scheme and parser results of the animacy feature in Russian and Arabic, two morphologicallyrich languages, in the spirit of the universal dependency framework (McDonald et al., 2013; de Marneffe et al., 2014). We explain the animacy hierarchies in both languages and make the case for the existence of five animacy types. We train a morphological analyzer on the annotated data and the results show a prediction f-measure for animacy of 95.39% for Russian and 92.71% for Arabic. We also use animacy along with other morphological tags as features to train a dependency parser, and the results show a slight improvement gained from animacy. We compare the impact of animacy on improving the dependency parser to other features found in nouns, namely, ‘gender’, ‘number’, and ‘case’. To our knowledge this is the first contrastive study of the impact of morphological features on the accuracy of a transition parser. A portion of our data (1,000 sentences for Arabic and Russian each, along with other languages) annotated according to the scheme described in this paper is made publicly available (https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-1983) as part of the CoNLL 2017 Shared Task on Multilingual Parsing (Zeman et al., 2017).

• Natural Language Processing