Session Program


  • 10 July 2017
  • 02:00PM - 04:00PM
  • Room: Borbonica
  • Chairs: Nicolas Marin, Anna Wilbik, Rui-Jorge Almeida and Daniel Sanchez

Linguistic Summarization and Description of Data

Abstract - Extracting useful and interpretable knowledge from raw data is a crucial issue that has been largely addressed by the data mining community especially. In this paper we provide an interactive data exploration approach that relies on two steps. First, a personalized linguistic summary of the data set concerned is built and displayed as a tag cloud. Then, exploration functionalities are provided on top of the summary to let the user discover interesting properties in the data as frequent/atypical/diversified associations between properties.
Abstract - We present a novel heuristic approach that defines fuzzy geographical descriptors using data gathered from a survey with human subjects. The participants were asked to provide graphical interpretations of the descriptors "north" and "south" for the Galician region (Spain). Based on these interpretations, our approach builds fuzzy descriptors that are able to compute membership degrees for geographical locations. We evaluated our approach in terms of efficiency and precision. The fuzzy descriptors are meant to be used as the cornerstones of a geographical referring expression generation algorithm that is able to linguistically characterize geographical locations and regions. This work is also part of a general research effort that intends to establish a methodology which reunites the empirical studies traditionally practiced in data-to-text and the use of fuzzy sets to model imprecision and vagueness in words and expressions for text generation purposes.
Abstract - Recently, we have presented a new technology for improving player experience in Computer Games by using players behaviour analysis and linguistic descriptions. Here, we explain the details about its implementation which have not been presented in any work before. Our implementation is based on a declarative version of the concept of computational perception network whose implementation has been performed by means of a fuzzy linguistic logic programming language named Bousi Prolog. Our aim is to show the potentiality of this kind of programming language in order to implement computational perceptions in computer games.
Abstract - This paper presents an experiment to show the importance of causal sentences in summaries. Presumably, causal sentences hold relevant information and thus summaries should contain them. We perform an experiment to refute or validate this hypothesis. We have selected 28 medical documents to extract and analyze causal and conditional sentences from medical texts. Once retrieved, classic metrics are used to determine the relevance of the causal content among all the sentences in the document and, so, to evaluate if they are important enough to make a better summary. Finally, a comparison table to explore the results is showed and some conclusions are outlined.
Abstract - In this paper we propose bounding conditions that characterize different families of specificity measures. The proposed conditions are associated to particular semantics that provide us with different criteria for choosing the most appropriate specificity measure for a certain use. In particular, we distinguish predicate-like measures, mostly useful for providing a fuzzy classification of fuzzy sets between specific and non-specific, and index-like measures, which are more appropriate for providing an order relation between fuzzy sets in terms of their specificity. An intermediate class of measures is also studied, where different bounds are associated to values of the cardinality of fuzzy sets for which specificity is expected to be 0.
Abstract - Generation of linguistic summaries that are compact, short and relevant to the user remains an open challenge. In this paper, we propose a novel method for improving the generation of linguistic summaries inspired by the a-priori algorithm and the degree of appropriateness. The method generates all true summaries with related predicates in the summarizer, resulting in a small set of linguistic summaries, whose presentation to the user is compact. We tested our method on three real world data sets. The results indicate that our proposed approach is a good alternative to previous methods suggested for generating linguistic summaries.