Text similarity by using GloVe word vector representations
Word embeddings are word representations in the form of vectors that allow to maintain certain semantic information of the words. There exist different ways of taking profit of the semantic information the words have, as there exist different ways of generating the word vectors that represent those...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/90045 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/90045 |
| Access Level: | acceso abierto |
| Palabra clave: | Similitud entre textos Vectores de palabras en español Similitud semántica Diferencia semántica Representación vectorial de frases Word vector representations Word embeddings Text similarity Spanish word embeddings Semantic difference Phrase embeddings Phrase similarity Global Vectors GloVe Similitud entre frases LENGUAJES Y SISTEMAS INFORMATICOS Máster Universitario en Inteligencia Artificial, Reconocimiento de Formas e Imagen Digital-Màster Universitari en Intel·ligència Artificial, Reconeixement de Formes i Imatge Digital |
| Sumario: | Word embeddings are word representations in the form of vectors that allow to maintain certain semantic information of the words. There exist different ways of taking profit of the semantic information the words have, as there exist different ways of generating the word vectors that represent those words (e.g. Word2Vec model vs. GloVe model). By using the semantic information the word embeddings capture, we can build approximations to compare semantic information between phrases or even documents instead of words. In this project, we propose the use of the GloVe tool, presented by Stanford University, to train Spanish word embeddings, use them to compare semantic differences between Spanish phrases and compare the accuracy of the system with prior results in which other models were used, for example, Word2Vec. |
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