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- W3008288511 abstract "Having systems that help the guilt decision-making or legal innocence, is of vital importance in law. In this paper, an ontological model of rapid prototyping is presented, which allows to infer knowledge from connections of propositions and legal proofs. These connections, propositions, and tests have been modeled in a prototype, which was modeled easily and quickly by means of the ontological semantic relation. Here we intend to address the emotional burden in sentences in relation if it has a positive, negative or neutral emotional charge. To do this, the paragraphs are analyzed with text mining algorithms. They are classified according to their emotional charge, which is known as polarity. Ontologies are an excellent tool when modeling judicial narratives that lead to the guilt or innocence of a person. In our proposal, we will identify the polarity of the text and then try to interpret this as part of positive or negative emotion. We will not get to elucidate in this work what kind of emotion is related to polarity because it is also true that human behavior, thoughts, and feelings cannot be modeled automatically or perfectly. Nor do we intend to ensure the polarity of a text because even this, depends on the mood of the person. As a result, there is a guide to decision making by a human expert as a new form of persuasive communication." @default.
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- W3008288511 date "2020-01-01" @default.
- W3008288511 modified "2023-09-26" @default.
- W3008288511 title "First Steps in the Automatic Classification of Legal Sentences" @default.
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- W3008288511 doi "https://doi.org/10.1007/978-981-15-1084-7_5" @default.
- W3008288511 hasPublicationYear "2020" @default.
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