Pull your treebank up by its own bootstraps

National audienceWe analyze the performance of recent neural syntactic parsers in the task of bootstrapping a treebank, i.e. training and analyzing iteratively in order to enhance speed and quality of the human syntactic analysis. By conducting an extensive and heuristically guided search in the vast grid of options (parser, embedding, configuration, epochs, batch size, size of training set, annotation scheme, language, evaluation method…), we determine the best performing parser configurations: UDify and Trankit share the podium depending on the size of the training set. We also show how these results are integrated into the annotation tool ArboratorGrew, and we propose some preliminary measures that allow predicting the quality of the parse for a new language

Pull your treebank up by its own bootstraps

National audienceWe analyze the performance of recent neural syntactic parsers in the task of bootstrapping a treebank, i.e. training and analyzing iteratively in order to enhance speed and quality of the human syntactic analysis. By conducting an extensive and heuristically guided search in the vast grid of options (parser, embedding, configuration, epochs, batch size, size of training set, annotation scheme, language, evaluation method…), we determine the best performing parser configurations: UDify and Trankit share the podium depending on the size of the training set. We also show how these results are integrated into the annotation tool ArboratorGrew, and we propose some preliminary measures that allow predicting the quality of the parse for a new language

Joint Annotation of Morphology and Syntax in Dependency Treebanks

International audienceIn this paper, we compare different ways to annotate both syntactic and morphological relations in a dependency treebank. We propose new formats we call mSUD and mUD, compatible with the Universal Dependencies (UD) schema for syntactic treebanks. We emphasize on mSUD rather than mUD, the former being based on distributional criteria for the choice of the head of any combination, which allows us to clearly encode the internal structure of a word, that is, the derivational path. We investigate different problems posed by a morph-based annotation, concerning tokenization, choice of the head of a morph combination, relations between morphs, additional features needed, such as the token type differentiating roots and derivational and inflectional affixes. We show how our annotation schema can be applied to different languages from polysynthetic languages such as Yupik to isolating languages such as Chinese

Universal Dependencies 2.3

Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008)