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Estudio comparativo de herramientas de procesamiento de Lenguaje Natural Open Source y Software propietario para el desarrollo de aplicaciones basadas en inteligencia artificial.
| dc.contributor.advisor | Cevallos Monar, Carlos Alfredo | |
| dc.contributor.author | Angamarca Peña, Klever Eduardo | |
| dc.date.accessioned | 2023-11-06T19:36:52Z | |
| dc.date.available | 2023-11-06T19:36:52Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | http://dspace.utb.edu.ec/handle/49000/15028 | |
| dc.description | The goal of this comparative study is to identify the main Open-Source and Proprietary Natural Language Processing (NLP) tools and to analyze one Open-Source NLP tool and one Proprietary tool in terms of their functionality, efficiency, price, and ease of implementation for the development of artificial intelligence-based applications. To this end, a research was carried out on Natural Language Processing and its main applications. Regarding Open-Source tools, Spacy, NLTK, Gensim, and Spark NLP were selected highlighting their main features. In the same way, this process was carried out with Google Natural Language API, IBM Watson Natural Language Understanding, Amazon Comprehend, and Azure AI API, which are proprietary tools. The advantages and disadvantages of using these two types of tools were compared, thus facilitating informed decision-making. In terms of practical analysis two tools were selected: Google Natural Language API with the Google Cloud platform and the Google Colab platform which was used to run Spacy. It is concluded that Open-Source and proprietary tools offer excellent results in terms of text analysis. The selection of one tool or another should be based on the specific needs and characteristics of each project, as well as the available budget as a company or developer. | es_ES |
| dc.description | The goal of this comparative study is to identify the main Open-Source and Proprietary Natural Language Processing (NLP) tools and to analyze one Open-Source NLP tool and one Proprietary tool in terms of their functionality, efficiency, price, and ease of implementation for the development of artificial intelligence-based applications. To this end, a research was carried out on Natural Language Processing and its main applications. Regarding Open-Source tools, Spacy, NLTK, Gensim, and Spark NLP were selected highlighting their main features. In the same way, this process was carried out with Google Natural Language API, IBM Watson Natural Language Understanding, Amazon Comprehend, and Azure AI API, which are proprietary tools. The advantages and disadvantages of using these two types of tools were compared, thus facilitating informed decision-making. In terms of practical analysis two tools were selected: Google Natural Language API with the Google Cloud platform and the Google Colab platform which was used to run Spacy. It is concluded that Open-Source and proprietary tools offer excellent results in terms of text analysis. The selection of one tool or another should be based on the specific needs and characteristics of each project, as well as the available budget as a company or developer. | es_ES |
| dc.description.abstract | El objetivo de este estudio comparativo es identificar las principales herramientas de Procesamiento de Lenguaje Natural Open Source y Software Propietario y analizar una herramienta de NLP Open Source y una de Software Propietario en términos de su funcionalidad, eficiencia, precio y facilidad de implementación para el desarrollo de aplicaciones basadas en Inteligencia Artificial (AI). Empezamos con la recolección de información respecto al Procesamiento de Lenguaje Natural y sus principales aplicaciones. En cuanto a las herramientas Open Source elegimos Spacy, NLTK, Gensim y Spark NLP destacando sus principales características, de la misma manera realizamos este proceso con Google Natural Language API, IBM Watson Natural Language Understanding, Amazon Comprehend y Azure AI API las cuales son herramientas de origen propietario, comparamos las ventajas y desventajas del uso de estos dos tipos de herramientas facilitando así una toma de decisiones informada. Para el análisis práctico seleccionamos dos herramientas, Google Natural Language API junto con la plataforma de Google Cloud y la plataforma de Google Colab la utilizamos para ejecutar Spacy. Se concluye que las herramientas Open Source y de Software Propietario ofrecen excelentes resultados en cuanto al análisis de texto y la selección de una herramienta u otra debe basarse en las necesidades y características específicas de cada proyecto además del presupuesto que se disponga como empresa o desarrollador. | es_ES |
| dc.format.extent | 34 p. | es_ES |
| dc.language.iso | es | es_ES |
| dc.publisher | Babahoyo: UTB-FAFI. 2023 | es_ES |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Ecuador | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/ec/ | * |
| dc.subject | NLP | es_ES |
| dc.subject | Spacy | es_ES |
| dc.subject | Open Source | es_ES |
| dc.subject | Google API | es_ES |
| dc.subject | Google Cloud | es_ES |
| dc.subject | AI | es_ES |
| dc.title | Estudio comparativo de herramientas de procesamiento de Lenguaje Natural Open Source y Software propietario para el desarrollo de aplicaciones basadas en inteligencia artificial. | es_ES |
| dc.type | bachelorThesis | es_ES |
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