In this paper we present the OnForumS corpus developed for the shared task of the same name on Online Forum Summarisation (OnForumS at MultiLing’15). The corpus consists of a set of news articles with associated readers’ comments from The Guardian (English) and La Repubblica (Italian). It comes with four levels of annotation: argument structure, comment-article linking, sentiment and coreference. The former three were produced through crowdsourcing, whereas the latter, by an experienced annotator using a mature annotation scheme. Given its annotation breadth, we believe the corpus will prove a useful resource in stimulating and furthering research in the areas of Argumentation Mining, Summarisation, Sentiment, Coreference and the interlinks therein.

This paper describes how we built a dependency Treebank for questions. The questions for the Treebank were drawn from questions from the TREC 10 QA task and from Yahoo! Answers. Among the uses for the corpus is to train a dependency parser achieving good accuracy on parsing questions without hurting its overall accuracy. We also explore active learning techniques to determine the suitable size for a corpus of questions in order to achieve adequate accuracy while minimizing the annotation efforts.

This paper describes SW1, the first version of a semantically annotated snapshot of the English Wikipedia. In recent years Wikipedia has become a valuable resource for both the Natural Language Processing (NLP) community and the Information Retrieval (IR) community. Although NLP technology for processing Wikipedia already exists, not all researchers and developers have the computational resources to process such a volume of information. Moreover, the use of different versions of Wikipedia processed differently might make it difficult to compare results. The aim of this work is to provide easy access to syntactic and semantic annotations for researchers of both NLP and IR communities by building a reference corpus to homogenize experiments and make results comparable. These resources, a semantically annotated corpus and a “entity containment” derived graph, are licensed under the GNU Free Documentation License and available from http://www.yr-bcn.es/semanticWikipedia