Reminiscence therapy is a non-pharmacological intervention that helps mitigate unstable psychological and emotional states in patients with Alzheimer’s disease, where past experiences are evoked through conversations between the patients and their caregivers, stimulating autobiographical episodic memory. It is highly recommended that people with Alzheimer regularly receive this type of therapy. In this paper, we describe the development of a conversational system that can be used as a tool to provide reminiscence therapy to people with Alzheimer’s disease. The system has the ability to personalize the therapy according to the patients information related to their preferences, life history and lifestyle. An evaluation conducted with eleven people related to patient care (caregiver = 9, geriatric doctor = 1, care center assistant = 1) shows that the system is capable of carrying out a reminiscence therapy according to the patient information in a successful manner.
In this research is introduced a methodology for the ontology automatic construction for the pedagogical domain based on the denition of two lexical resources: a domain corpus and, the use of a domain dictionary. In this research, the pedagogical corpus was building manually, where aspects such as learning strategies, intelligences types and learning styles were included. After that, the corpus is processed for extracting a list of concepts linked to the domain predened classes. Each concept is searched in ve dictionaries, including synonyms. This process is automatically performed, removing close words, words with low frequency. As result of this process, is building a domain dictionary which include concepts related with each domain class. The preliminary results show an anity between the corpus and the dictionary, which is a very important resource for an ontology denition process. As future work, these resources will be used for detect classes in a ontology learning process for the pedagogical domain using machine learning techniques.
The aim of this article is to contextualize and describe the gathering and annotation of a conventual Hispanic and Novo Hispanic texts corpus for emotions identification. Such corpus will be the dataset for an emotions identification model based on machine learning ∖ deep learning techniques. Furthermore, this document describes several exploratory experiments carried out on the corpus. Within these experiments, it is described how the corpus is also used to obtain a lexicon mapped to polarities and emotions, and how some of the documents are hand-labeled by experts for the evaluation of the Machine Learning ∖ Deep learning -based emotion classification model. Finally, the future uses and experiments with said corpus are described.
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