The high complexity of natural language and the huge amount of human and temporal resources necessary for producing the grammars lead several researchers in the area of Natural Language Processing to investigate various solutions for automating grammar generation and updating processes. Many algorithms for Context-Free Grammar inference have been developed in the literature. This paper provides a survey of the methodologies for inferring context-free grammars from examples, developed by researchers in the last decade. After introducing some preliminary definitions and notations concerning learning and inductive inference, some of the most relevant existing grammatical inference methods for Natural Language are described and classified according to the kind of presentation (if text or informant) and the type of information (if supervised, unsupervised, or semi-supervised). Moreover, the state of the art of the strategies for evaluation and comparison of different grammar inference methods is presented. The goal of the paper is to provide a reader with introduction to major concepts and current approaches in Natural Language Learning research.
Fake news detection has gained increasing importance among the research community due to the widespread diffusion of fake news through media platforms. Many dataset have been released in the last few years, aiming to assess the performance of fake news detection methods. In this survey, we systematically review twenty-seven popular datasets for fake news detection by providing insights into the characteristics of each dataset and comparative analysis among them. A fake news detection datasets characterization composed of eleven characteristics extracted from the surveyed datasets is provided, along with a set of requirements for comparing and building new datasets. Due to the ongoing interest in this research topic, the results of the analysis are valuable to many researchers to guide the selection or definition of suitable datasets for evaluating their fake news detection methods.
The increasing need to access information everywhere and at any time leads us to believe that future user interfaces, through which users interact with pervasive computing systems, must address both device and modality independence. The pervasive computing paradigm sees almost every object in the everyday environment as a system able to communicate with users and other systems in their own languages. The interaction between users and systems is therefore typically multimodal. The main challenge of multimodal interaction, that is also the main topic of this paper, lies in developing a framework that is able to process information derived from whatever input modalities, giving these inputs an appropriate representation and integrating these individual representations into a joint semantic interpretation. A description of this multimodal pervasive framework will be given in this paper, along with some details of its application in Ambient Assisted Living and the usability test that was implemented to validate its effectiveness.
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