{"id":15797823,"url":"https://github.com/qequ/clustering_nlp","last_synced_at":"2025-03-31T19:45:15.529Z","repository":{"id":115064722,"uuid":"407372482","full_name":"qequ/clustering_nlp","owner":"qequ","description":"Agrupación de palabras semejantes dado un corpus","archived":false,"fork":false,"pushed_at":"2021-09-25T00:19:39.000Z","size":22253,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-10-12T00:42:25.370Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/qequ.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-09-17T02:01:45.000Z","updated_at":"2021-09-25T00:19:41.000Z","dependencies_parsed_at":"2023-05-04T00:48:59.899Z","dependency_job_id":null,"html_url":"https://github.com/qequ/clustering_nlp","commit_stats":{"total_commits":16,"total_committers":1,"mean_commits":16.0,"dds":0.0,"last_synced_commit":"f727741d23fcd72c05f9e9885c586ee17606faa2"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/qequ%2Fclustering_nlp","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/qequ%2Fclustering_nlp/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/qequ%2Fclustering_nlp/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/qequ%2Fclustering_nlp/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/qequ","download_url":"https://codeload.github.com/qequ/clustering_nlp/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246531986,"owners_count":20792735,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-05T00:21:06.927Z","updated_at":"2025-03-31T19:45:15.510Z","avatar_url":"https://github.com/qequ.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Text Mining - Clustering\nAlumno: Alvaro Frias Garay\n\n# Objetivo \nEncontrar grupos de palabras similares en un corpus de texto\n\n# Detalles técnicos\nse utilizó [el corpus SBWCE de Cristian Cardellino](https://crscardellino.ar/resources/nlp/2016/02/06/spanish-billion-words-corpus-and-embeddings.html) y las siguientes tecnologías:\n\n* Spacy\n* Gensim\n* scikit-learn\n\n# Procedimiento\n\n## Preprocesamiento del corpus\nSe procesaron las oraciones agrupadas como tokens y se les dio el siguiente tratamiento;\n\n* Se removieron stopwords y signos de puntuación.\n* Se quitaron tokens no alfabéticos y de un largo de palabra menor a 3.\n* Se removieron pronombres.\n\n## Vectorización\n\nSe utilizaron _Word Embeddings Neuronales_, Word2Vec, para crear vectores de palabras a partir del corpus dado.\n\n## Tratamiento de la matriz de Word2Vec\nSe la normalizó y se quitaron dimensiones con poca varianza\n\n## Clustering\nSe utilizó el algoritmo de K-means tomando una _ventana_ de 5, una frecuencia mínima de 5 y un número de clusters de 25.\n\n## Resultados\n A continuación una muestra de palabras agrupadas en clusters\n\n```\nCluster 0\nWords: mediodía, perdóname, bárbara, apresuré, visitarlas, dimir, créanmir, bendiga, vedado, mencioné, retornar, jurar, vieja, opción\n\n```\n\n```\nCluster 1\nWords: seguro, ocurrir, vivir, habitación, mujer, bessie, noche, deseo, amigo, recibir, mano, puerta, opinión, hija, asunto, quedar, hijo, dar, pequeño, cuarto, forma, ser, atención, mirada, aspecto,\n```\n```\nCluster 2\nWords: acaso, contestar, rato, noticia, lamentar, alegrar, amable, faltar, vuelta, comprender, criado, satisfacción, separar, sonrisa\n```\n\n```\nCluster 3\nWords: elinor, señora, marianne, haber, hermana, casa, sentir, madre, deber, hacer,\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqequ%2Fclustering_nlp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fqequ%2Fclustering_nlp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqequ%2Fclustering_nlp/lists"}