Autotext: autoML for text classification
Non-experts in Machine Learning research have an increasing demand for easy-to-use methods to model solutions that use the large amounts of data available today, where such solutions are expected to perform at least as well as one build by a human with profound knowledge of ML and statistics. AutoML...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2019 |
| País: | México |
| Institución: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repositorio: | Repositorio Institucional del INAOE |
| Idioma: | inglés |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/1950 |
| Acceso en línea: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1950 |
| Access Level: | acceso abierto |
| Palabra clave: | info:eu-repo/classification/Inspec/AutoML info:eu-repo/classification/Inspec/Text classification info:eu-repo/classification/Inspec/Meta - learning info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 info:eu-repo/classification/cti/120323 |
| Sumario: | Non-experts in Machine Learning research have an increasing demand for easy-to-use methods to model solutions that use the large amounts of data available today, where such solutions are expected to perform at least as well as one build by a human with profound knowledge of ML and statistics. AutoML is the area that investigates the automation of Machine Learning. Some state-of-the-art methods for approaching this problem are already available, nonetheless none of them concentrate on the challenges of Natural Language Processing. This work comprises an extensive study in text classification where 81 different problems were approached with the most commonly used algorithms. Leveraging the obtained metadata from these experiments, a method that automatically builds pipelines for classifying text documents is proposed. This method contemplates the optimization of a classification model and its hyper-parameters as well as the selection of the representation vector for a text in a given set of unprocessed text documents. A characterization for tasks is introduced as part of this method, but the reach of this novel description is not limited to AutoML problems. Results in our experimentation show that the proposed AutoML method and the novel characterization outperform previous approaches for automating text classification and under certain circumstances obtain comparable results to state-of-theart models. Being one of the first works to explore AutoML in NLP, several further questions can be derived from this thesis with potential impact for both fields. |
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