Distributional models and semantic spaces represent a core topic in contemporary computational linguistics for their impact on advanced tasks and on other knowledge fields (such as social science and the humanities). Previous workshops in this area (e.g. the workshop on ‘Contextual Information in Semantic Space Models’, 2007, and the ESSLLI workshop on ‘Distributional Lexical Semantics’, 2008) reflect a still growing interest in the area in the last years.

The goal of the GEMS workshop is to further stimulate research on semantic spaces and distributional methods for NLP, by adopting an interdisciplinary approach to allow a proper exchange of ideas, results and resources among often independent communities. In particular, the workshop will provide a common ground for a fruitful discussion among experts of distributional approaches, collocational corpus analysis and machine learning; researchers interested in the use of statistical models in NLPapplications (e.g. question answering, summarization and textual entailment) and in other fields of science; and experts in formal computational semantics.

The workshop aims at gathering contemporary contributions to large scale problems in meaning representation, acquisition and use, based on distributional and vector space models. The workshop will also explore the impact of such techniques on complex linguistic tasks, such as linguistic knowledge acquisition, semantic role labeling, textual entailment recognition, question answering, document understanding/summarization and ontology learning.