Ricardo Ribeiro

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This paper describes an ontology for the cooking domain, reporting on the ontology building process, its life cycle, applied methodologies, taken decisions and achieved results. In the past, our research group built a generic dialogue system able to manage specific devices at home, such as TVs, lamps and windows. The cooking domain appeared as an(More)
To improve the quality of the speech produced by a Text-toSpeech (TTS) system, it is important to obtain the maximum amount of information from the input text that may help in this task. This covers a wide range of possibilities that can go from the simple conversion of non orthographic items to more complex syntactic and semantic analysis. In this paper,(More)
In automatic summarization, centrality-asrelevance means that the most important content of an information source, or of a collection of information sources, corresponds to the most central passages, considering a representation where such notion makes sense (graph, spatial, etc.). We assess the main paradigms and introduce a new centrality-based relevance(More)
Finding the starting time of musical notes in an audio signal, that is, to perform onset detection, is an important task as this information can be used as the basis for high-level musical processing tasks. Many different methods exist to perform onset detection. However their results depend on a Peak Selection step that makes the decision whether an onset(More)
In general, centrality-based retrieval models treat all elements of the retrieval space equally, which may reduce their effectiveness. In the specific context of extractive summarization (or important passage retrieval), this means that these models do not take into account that information sources often contain lateral issues, which are hardly as important(More)
The purpose of this paper is to present the development of a morphossyntactic disambiguation system (or part-of-speech tagging system) which is intended to be used as a component of a Text-to-Speech (TTS) system for European Portuguese. In the development of the tagger, we compared two approaches: a probabilistic-based approach and a hybrid approach.(More)
We assess the performance of generic text summarization algorithms applied to films and documentaries, using the well–known behavior of summarization of news articles as reference. We use three datasets: (i) news articles, (ii) film scripts and subtitles, and (iii) documentary subtitles. Standard ROUGE metrics are used for comparing generated summaries(More)
Libraries have large growing book collections. Library users have difficulty in browsing the whole collection when choosing new books to read, particularly when looking for books without a defined goal. In this case, recommendation systems come in hand and play an important role in improving library usability. Recommendations are based on ratings and the(More)
Recommendation is an important research area that relies on the availability and quality of the data sets in order to make progress. This paper presents a comparative study between Movielens, a movie recommendation data set that has been extensively used by the recommendation system research community, and LitRec, a newly created data set for content(More)