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Argument mining consists in the automatic identification of argumentative structures in texts. In this work we leverage existing discourse-level annotations to facilitate the identification of argumentative components and relations in scientific texts, which has been recognized as a particularly challenging task. We propose a new annotation schema and use it to augment a corpus of computational linguistics abstracts that had previously been annotated with discourse units and relations. Our initial experiments with the enriched corpus confirm the potential value of incorporating discourse information in argument mining tasks. In order to tackle the limitations posed by the lack of corpora containing both discourse and argumentative annotations we explore two transfer learning approaches in which discourse parsing is used as an auxiliary task when training argument mining models. In this case, as no discourse information is used as input, the resulting models could be used to predict the argumentative structure of unannotated texts. This work is (partly) supported by the Spanish Government under the María de Maeztu Units of Excellence Programme (MDM-2015-0502) and the Research and Innovation Agency of Uruguay (ANII). This work is (partly) supported by the Spanish Government under the Mar´ıa de Maeztu Units of Excellence Programme (MDM-2015-0502) and the Research and Innovation Agency of Uruguay (ANII).

We describe our work on information extraction from multiple sources for the Multimedia Indexing and Searching Environment, a project aiming at developing technology to produce formal annotations about essential events in multimedia programme material. The creation of a composite index from multiple and multi-lingual sources is a unique aspect of this project. The domain chosen for tuning the software components and testing is football. Our information extraction system is based on the use of finite state machinery pipelined with full semantic analysis and discourse interpretation.