Artificial Intelligence (AI) has always had a general goal of allowing for machines to “understand” documents.  The term “Machine Reading” focuses on automated reasoning based mechanisms which, based on a level of semantic interpretation of textual artifacts, have the ability to provide some form of probabilistic or logical inference. In so doing a machine learning system should be able to complete a specific task based on this deeper semantic oriented analysis. A particular focus of semantic interpretation based on natural language documents may also draw on the strong body of research in the field of Computational Linguistics. Clearly it is possible to envisage a whole gamut of various inference tasks and it’s apparent that Machine Reading will become a new and exciting field in its own right.

The increasing rise of social media and user generated content has spurred a number of novel techniques for text analysis in areas such as textual entailment, sentiment analysis, information diffusion, topic detection and tracking and profiling, all of which aim to provide a deeper level of semantic analysis of textual content. This workshop aims to draw together researchers and practitioners from various disciplines who have an interest in machine reading in general and machine reading for the purpose of semantic social media content analysis in particular.

The workshop will interest researchers in the following key areas: Information retrieval, Natural Language Processing, Computational Linguistics, Computational Semantics, Knowledge Representation, Knowledge Engineering, Logical or Probabilistic Reasoning, Information Extraction, Data Mining/Machine learning, Semantic Web, Semantic Content Analytics and Cross-media Content Analytics.