Gerard Casamayor

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Natural Language Generation (NLG) is concerned with transforming some formal content input into a natural language output, given some communicative goal. Although this input has taken many forms and representations over the years, it is the semantic/conceptual representations that have always been considered as the “natural” starting ground for NLG.(More)
Collocations play a significant role in second language acquisition. In order to be able to offer efficient support to learners, an NLP-based CALL environment for learning collocations should be based on a representative collocation error annotated learner corpus. However, so far, no theoretically-motivated collocation error tag set is available. Existing(More)
With their abstract vocabulary and overly long sentences, patent claims, like several other genres of legal discourse, are notoriously difficult to read and comprehend. The enormous number of both native and non-native users reading patent claims on a daily basis raises the demand for means that make them easier and faster to understand. An obvious way to(More)
So far, there has been little success in Natural Language Generation in coming up with general models of the content selection process. Nonetheless, there has been some work on content selection that employ Machine learning or heuristic search. On the other side, there is a clear tendency in NLG towards the use of resources encoded in standard Semantic Web(More)
Environmental and meteorological conditions are of utmost importance for the population, as they are strongly related to the quality of life. Citizens are increasingly aware of this importance. This awareness results in an increasing demand for environmental information tailored to their specific needs and background. We present an environmental information(More)
The Stanford Coreference Resolution System (StCR) is a multi-pass, rule-based system that scored best in the CoNLL 2011 shared task on general discourse coreference resolution. We describe how the StCR has been adapted to the specific domain of patents and give some cues on how it can be adapted to other domains. We present a linguistic analysis of the(More)
Citizens are increasingly aware of the influence of environmental and meteorological conditions on the quality of their life. This results in an increasing demand for personalized environmental information, i.e., information that is tailored to citizens' specific context and background. In this work we describe the development of an environmental(More)
We present an approach to content selection that works on an ontology-based knowledge base developed independently from the task at hand, i.e., Natural Language Generation. Prior to content selection, a stage akin to signal analysis and data assessment used in the generation from numerical data is performed for identifying and abstracting patterns and(More)