Abstract
Our world is becomes more and more globalized. Frontiers are not what they once were, thanks to the discovery of the Internet. Knowing several languages is something that is almost essential. When we are studying a new language, we focus on learning vocabulary and all about syntax. In other words, this means we want to know how to coordinate and join words to create sentences and concepts. But language has another dimension that we usually overlook: semantics. This dimension is very important in language because it gives meaning to words. Semantics study could be as wide and complex as language to analyze itself. Therefore, in order to simplify our tasks, we have started with a finite set of terms, Specifically we have the first five levels set up by an English language academy. The objective of this research presented here was the development of a system that allows to analyze and detect semantic errors in simple sentences and syntactically correct to support English learning. Basically, the system we have developed is formed by two different components: a semantic analyzer and a web interface. The semantic analyzer has been built using the programming language Java and utilizes ontology and a reasoner over it. The use of ontologies allows the classification and categorization of different words in a language so that we can apply a reasoner to classified concepts to infer new knowledge and to detect whether a sentence makes sense or not.
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Acknowledgements
We thank Ms. Beatriz Angulo Capel for her involvement and assistance in the implementation tasks of the approach presented here.
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Martínez-Béjar, R., Mackenzie Rivero, A.J., Merchán Carreño, E.J. (2018). Advanced Semantics Processing-Based Information System to Support English Learning. In: Valencia-García, R., Alcaraz-Mármol, G., Del Cioppo-Morstadt, J., Vera-Lucio, N., Bucaram-Leverone, M. (eds) Technologies and Innovation. CITI 2018. Communications in Computer and Information Science, vol 883. Springer, Cham. https://doi.org/10.1007/978-3-030-00940-3_20
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