What is it about?
This paper presents the first results of a functional prototype implementing a linguistic model focused on regulations in Spanish. Its global architecture, the reasoning model, a case study and short statistics are provided for the prototype named PTAH. It mainly has a conversational robot linked to an Expert System by a module with many intelligent linguistic filters, implementing the reasoning model of an expert. It is focused in bylaws, regulations, jurisprudence and customized background representing entity mission, vision and profile. This Structure and model are generic enough to self adapt to any regulatory environment, but as a first step, it was limited to academic field. This way it is possible to limit the slang and data number. The foundations of the linguistic model are also outlined and the way the architecture implements the key features of the behavior. The cases presented are a few just to show the usability, flexibility and prospectives of this proposal.
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Why is it important?
Most of the NLP models are general enough to allow it to be applied to several specific problems, but that is acquired losing performance and precision. The current model proposes to generalize only the process but not the predictions itself, taking into account that natural language is very complex and depends on the topic and cultural environment.
Perspectives
NLP can be considered one of the topic most studied in the field of Computational Intelligence. Therefore many approaches have been presented up to now. The main failure consists in the need to explicit in some way the context information or the way the model learns how to infer meanings of words. This paper presents one of a series of approaches that intends to overcome the those problems by using implicit knowledge self extracted and and self organized not with neural networks but with Morphosyntactic Linguistic Wavelets.
Dr. Daniela López De Luise
CI2S Lab
Read the Original
This page is a summary of: Intelligent Chatter Bot for Regulation Search, Open Physics, January 2016, De Gruyter,
DOI: 10.1515/phys-2016-0053.
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