The paper presents a discussion on the main linguistic phenomena of user-generated texts found in web and social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework. Given on the one hand the increasing number of treebanks featuring user-generated content, and its somewhat inconsistent treatment in these resources on the other, the aim of this paper is twofold: (1) to provide a short, though comprehensive, overview of such treebanks - based on available literature - along with their main features and a comparative analysis of their annotation criteria, and (2) to propose a set of tentative UD-based annotation guidelines, to promote consistent treatment of the particular phenomena found in these types of texts. The main goal of this paper is to provide a common framework for those teams interested in developing similar resources in UD, thus enabling cross-linguistic consistency, which is a principle that has always been in the spirit of UD.

We present a freely available, genre-balanced English web corpus totaling 4M tokens and featuring a large number of high-quality automatic annotation layers, including dependency trees, non-named entity annotations, coreference resolution, and discourse trees in Rhetorical Structure Theory. By tapping open online data sources the corpus is meant to offer a more sizable alternative to smaller manually created annotated data sets, while avoiding pitfalls such as imbalanced or unknown composition, licensing problems, and low-quality natural language processing. We harness knowledge from multiple annotation layers in order to achieve a “better than NLP” benchmark and evaluate the accuracy of the resulting resource.

Part of speech tagging is a fundamental NLP task often regarded as solved for high-resource languages such as English. Current state-of-the-art models have achieved high accuracy, especially on the news domain. However, when these models are applied to other corpora with different genres, and especially user-generated data from the Web, we see substantial drops in performance. In this work, we study how a state-of-the-art tagging model trained on different genres performs on Web content from unfiltered Reddit forum discussions. We report the results when training on different splits of the data, tested on Reddit. Our results show that even small amounts of in-domain data can outperform the contribution of data an order of magnitude larger coming from other Web domains. To make progress on out-of-domain tagging, we also evaluate an ensemble approach using multiple single-genre taggers as input features to a meta-classifier. We present state of the art performance on tagging Reddit data, as well as error analysis of the results of these models, and offer a typology of the most common error types among them, broken down by training corpus.