Universal dependencies (UD) is a framework for morphosyntactic annotation of human language, which to date has been used to create treebanks for more than 100 languages. In this article, we outline the linguistic theory of the UD framework, which draws on a long tradition of typologically oriented grammatical theories. Grammatical relations between words are centrally used to explain how predicate–argument structures are encoded morphosyntactically in different languages while morphological features and part-of-speech classes give the properties of words. We argue that this theory is a good basis for cross-linguistically consistent annotation of typologically diverse languages in a way that supports computational natural language understanding as well as broader linguistic studies.

The paper describes our system submitted for the Workshop on PARSEME's Shared Task on automatic identification of verbal multiword expressions . It uses POS tagging and dependency parsing to identify single-and multi-token verbal MWEs in text. Our system is language-independent and competed on nine of the eighteen languages. Our paper describes how our system works and gives its error analysis for the languages it

In this paper, we present how the principles of universal dependencies and morphology have been adapted to Hungarian. We report the most challenging grammatical phenomena and our solutions to those. On the basis of the adapted guidelines, we have converted and manually corrected 1,800 sentences from the Szeged Treebank to universal dependency format. We also introduce experiments on this manually annotated corpus for evaluating automatic conversion and the added value of language-specific, i.e. non-universal, annotations. Our results reveal that converting to universal dependencies is not necessarily trivial, moreover, using languagespecific morphological features may have an impact on overall performance.

Uncertainty detection has been a popular topic in natural language processing, which manifested in the creation of several corpora for English. Here we show how the annotation guidelines originally developed for English standard texts can be adapted to Hungarian webtext. We annotated a small corpus of Facebook posts for uncertainty phenomena and we illustrate the main characteristics of such texts, with special regard to uncertainty annotation. Our results may be exploited in adapting the guidelines to other languages or domains and later on, in the construction of automatic uncertainty detectors. BackgroundDetecting uncertainty in natural language texts has received a considerable amount of attention in the last decade (Farkas et al., 2010;Morante and Sporleder, 2012). Several manually annotated corpora have been created, which serve as training and test databases of state-of-the-art uncertainty detectors based on supervised machine learning techniques. Most of these corpora are constructed for English, however, their domains and genres are diverse: biological texts (Medlock and Briscoe, 2007;Kim et al., 2008;Settles et al., 2008;Shatkay et al., 2008;Vincze et al., 2008;Nawaz et al., 2010) The diversity of the resources also manifests in the fact that the annotation principles behind the corpora might slightly differ, which led Szarvas et al. (2012) to compare the annotation schemes of three corpora (BioScope, FactBank and WikiWeasel) and they offered a unified classification of semantic uncertainty phenomena, on the basis of which these corpora were reannotated, using uniform guidelines. Some other uncertainty-related linguistic phenomena are described as discourse-level uncertainty in Vincze (2013). As a first objective of our paper, we will carry out a pilot study and investigate how these unified guidelines can be adapted to texts written in a language that is typologically different from English, namely, Hungarian.As a second goal, we will also focus on annotating texts in a new domain: social media textsapart from Wei et al. (2013) -have not been extensively investigated from the uncertainty detection perspective. As the use and communication through the internet is becoming more and more important in people's lives, the huge amount of data available from this domain is a valuable source of information for computation linguistics. However, processing texts from the web -especially social media texts from blogs, status updates, chat logs and comments -revealed that they are very challenging for applications trained on standard texts. Most studies in this area focus on English, for instance, sentiment analysis from tweets has been the focus of recent challenges (Wilson et al., 2013) and Facebook posts have been analysed from the perspective of computational psychology (Celli et al., 2013). A syntactically

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