3_Escuela_Verano_preprocesamiento.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNbuyPN2Y29okaTTBrGD9G2",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/orlandxrf/escuela-verano/blob/main/3_Escuela_Verano_preprocesamiento.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://raw.githubusercontent.com/orlandxrf/escuela-verano/main/img/logos.png\" height=\"100\" />\n",
        "\n",
        "<h1>Escuela de verano del Laboratorio de Estructuras y Dinámicas Sociales</h1>\n",
        "Escuela de Verano, 25-28 de Junio de 2024\n",
        "<hr>\n",
        "<h2><strong>Descubre el poder del PLN: aprende a pre-procesar, analizar y extraer información de textos<strong></h2>\n",
        "<hr>"
      ],
      "metadata": {
        "id": "oHT5goyLgg7X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocesamiento de archivos"
      ],
      "metadata": {
        "id": "cYWRBhBnvw8i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "En el preprocesamiento de archivos de textos incluyen los pasos para: limpieza de caracteres no deseados y/o eliminar palabras vacías (stop-words), la normalización que consiste de la tokenización, la lematización (reducir las palabras a su forma base) y el etiquetado POS (Part-of-Speech).\n",
        "\n",
        "Adicionalmente, se puede realizar un análisis de dependencias de los textos para identificar las relaciones entre las palabras."
      ],
      "metadata": {
        "id": "wzRUJyliv4VS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Obtener archivos"
      ],
      "metadata": {
        "id": "5-pNTFBiGXoQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para este taller se almacenan los archivos en [Github](https://github.com/orlandxrf/escuela-verano) adicional a todo el material usado.\n",
        "\n",
        "Para evitar subir los archivos a Google Colab cada vez que se quiera trabajar con el Notebook, se realizará el proceso de forma automática. Para la descarga de los archivos se utiliza el comando `wget`\n",
        "\n",
        "**Wget**, que significa \"World Wide Web GET\", es una utilidad de línea de comandos en sistemas operativos tipo Unix, como Linux, que se utiliza para descargar archivos de la web. Es una herramienta poderosa y versátil que permite descargar una amplia variedad de contenido.\n",
        "\n",
        "La sintaxis para descargar un archivo es la siguiente:\n",
        "\n",
        "```bash\n",
        "!wget -P data/ https://raw.githubusercontent.com/orlandxrf/escuela-verano/main/data/El_maravilloso_Mago_de_Oz.pdf\n",
        "```\n",
        "\n",
        "El resultado esperado por cada archivo descargado es similar al siguiente\n",
        "\n",
        "```bash\n",
        "--2024-06-22 18:35:06--  https://github.com/orlandxrf/escuela-verano/blob/main/data/El_maravilloso_Mago_de_Oz.pdf\n",
        "Resolving github.com (github.com)... 140.82.121.3\n",
        "Connecting to github.com (github.com)|140.82.121.3|:443... connected.\n",
        "HTTP request sent, awaiting response... 200 OK\n",
        "Length: unspecified [text/html]\n",
        "Saving to: ‘data/El_maravilloso_Mago_de_Oz.pdf’\n",
        "\n",
        "El_maravilloso_Mago     [ <=>                ] 241.79K  --.-KB/s    in 0.03s   \n",
        "\n",
        "2024-06-22 18:35:06 (8.05 MB/s) - ‘data/El_maravilloso_Mago_de_Oz.pdf’ saved [247596]\n",
        "```\n",
        "\n",
        "Es importante señalar que si la carpeta donde se desea almacenar el archivo no existe, se crea. En caso de descargar el archivo cuando previamente existe, lo que ocurre es que se sobre escribe el archivo descargado, es decir el archivo descargado reemplaza al previo, siempre y cuando tengan el mismo nombre."
      ],
      "metadata": {
        "id": "zldabsuBdNr1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# descargar los archivos utilizando \"wget\"\n",
        "\n",
        "#!wget -P data/ https://raw.githubusercontent.com/orlandxrf/escuela-verano/main/data/Escuela_de_verano.txt\n",
        "!wget -P data/ https://raw.githubusercontent.com/orlandxrf/escuela-verano/main/data/Los_Caudillos_de_1830.txt\n",
        "#!wget -P data/ https://raw.githubusercontent.com/orlandxrf/escuela-verano/main/data/ia_tweets.csv\n",
        "#!wget -P data/fake_news/ https://raw.githubusercontent.com/orlandxrf/escuela-verano/main/data/fake_news/dev.xlsx\n",
        "#!wget -P data/fake_news/ https://raw.githubusercontent.com/orlandxrf/escuela-verano/main/data/fake_news/train.xlsx\n",
        "#!wget -P data/fake_news/ https://raw.githubusercontent.com/orlandxrf/escuela-verano/main/data/fake_news/test.xlsx\n",
        "#!wget -P data/ https://github.com/orlandxrf/escuela-verano/blob/main/data/El_maravilloso_Mago_de_Oz.pdf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0PTIygDWJdVN",
        "outputId": "c6d3c3be-33bd-4933-ea93-68c33115f3ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-06-23 21:04:00--  https://raw.githubusercontent.com/orlandxrf/escuela-verano/main/data/Los_Caudillos_de_1830.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.111.133, 185.199.108.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 242209 (237K) [text/plain]\n",
            "Saving to: ‘data/Los_Caudillos_de_1830.txt.2’\n",
            "\n",
            "\r          Los_Caudi   0%[                    ]       0  --.-KB/s               \rLos_Caudillos_de_18 100%[===================>] 236.53K  --.-KB/s    in 0.04s   \n",
            "\n",
            "2024-06-23 21:04:00 (6.43 MB/s) - ‘data/Los_Caudillos_de_1830.txt.2’ saved [242209/242209]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Bibliotecas requeridas"
      ],
      "metadata": {
        "id": "GLyTpeMOAX35"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Las bibliotecas a utilizar se listan a continuación:\n",
        "\n",
        "* `re`. Para expresiones regulares y procesamiento de texto. Biblioteca instalada por default con Python.\n",
        "* `spacy`. [Spacy](https://spacy.io/usage) es una biblioteca completa para el procesamiento del lenguaje natural (PLN).\n",
        "* `matplotlib`. Biblioteca para graficación de datos.\n",
        "\n",
        "Instalar las bibliotecas con el comando `pip`"
      ],
      "metadata": {
        "id": "0mPEEqgcAfAQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# instalar la biblioteca spacy\n",
        "!pip install -U pip setuptools wheel\n",
        "!pip install -U spacy\n",
        "\n",
        "# decargar el modelo en idioma español\n",
        "!python -m spacy download es_core_news_sm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bPCQIBAtArS7",
        "outputId": "4066899d-1280-4180-9b66-2edbafe6c24b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
            "\u001b[0mCollecting es-core-news-sm==3.7.0\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/es_core_news_sm-3.7.0/es_core_news_sm-3.7.0-py3-none-any.whl (12.9 MB)\n",
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            "Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (2024.6.2)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.2.2->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (0.7.11)\n",
            "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.2.2->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (0.1.5)\n",
            "Requirement already satisfied: click>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (8.1.7)\n",
            "Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (1.5.4)\n",
            "Requirement already satisfied: rich>=10.11.0 in /usr/local/lib/python3.10/dist-packages (from typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (13.7.1)\n",
            "Requirement already satisfied: cloudpathlib<1.0.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from weasel<0.5.0,>=0.1.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (0.18.1)\n",
            "Requirement already satisfied: smart-open<8.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from weasel<0.5.0,>=0.1.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (7.0.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (2.1.5)\n",
            "Requirement already satisfied: marisa-trie>=0.7.7 in /usr/local/lib/python3.10/dist-packages (from language-data>=1.2->langcodes<4.0.0,>=3.2.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (1.2.0)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (2.16.1)\n",
            "Requirement already satisfied: wrapt in /usr/local/lib/python3.10/dist-packages (from smart-open<8.0.0,>=5.2.1->weasel<0.5.0,>=0.1.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (1.14.1)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (0.1.2)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('es_core_news_sm')\n",
            "\u001b[38;5;3m⚠ Restart to reload dependencies\u001b[0m\n",
            "If you are in a Jupyter or Colab notebook, you may need to restart Python in\n",
            "order to load all the package's dependencies. You can do this by selecting the\n",
            "'Restart kernel' or 'Restart runtime' option.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# instalar grapviz para construir y visualizar árboles de dependencias\n",
        "# usualmente ya se encuentra instalado\n",
        "!apt-get install graphviz\n",
        "!pip install graphviz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5HhraiwLo3gI",
        "outputId": "1ffe7325-5636-4b99-9b31-a0854eb19eae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "graphviz is already the newest version (2.42.2-6).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 45 not upgraded.\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (0.20.3)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Importar bibliotecas"
      ],
      "metadata": {
        "id": "qekz4lIhBlT_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import spacy\n",
        "import re\n",
        "from spacy.lang.es.stop_words import STOP_WORDS\n",
        "from IPython.display import Image"
      ],
      "metadata": {
        "id": "e-BU71wrBoF0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# cargar el modelo en español de spacy\n",
        "nlp = spacy.load('es_core_news_sm')"
      ],
      "metadata": {
        "id": "2p6lUUWPknQM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cargar datos del archivo"
      ],
      "metadata": {
        "id": "Xa0VlcpR_jQt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Leer el archivo a *preprocesar*, si es un archivo de texto plano (`.txt`) leer el archivo y almacenar el contenido en una estructura de Python (lista o diccionario).\n",
        "\n",
        "En caso de leer un archivo estructurado con formato `.csv` o `.tsv` utilizar la biblioteca `pandas` para la obtención de la información del archivo."
      ],
      "metadata": {
        "id": "Wz6LF9Db_x-7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# archivo de texto plano \"Los_Caudillos_de_1830.txt\"\n",
        "\n",
        "ruta_archivo = \"data/Los_Caudillos_de_1830.txt\"\n",
        "datos = []\n",
        "\n",
        "with open(ruta_archivo) as archivo:\n",
        "  for linea in archivo:\n",
        "    datos.append(linea)\n",
        "\n",
        "print(f\"{len(datos):,} renglones en el archivo: '{ruta_archivo}'\")\n",
        "\n",
        "# salida esperada:\n",
        "# 6,100 renglones en el archivo: 'data/Los_Caudillos_de_1830.txt'\n",
        "\n",
        "# visualizar los primeros 10 elementos almacenados\n",
        "print(datos[0:11])\n",
        "# salida esperada:\n",
        "# ['                              PÍO BAROJA\\n', '\\n', '\\n',\n",
        "# '                                  LOS\\n', '                               CAUDILLOS\\n',\n",
        "# '                                DE 1830\\n', '\\n', '                                NOVELA\\n',\n",
        "# '\\n', '\\n', '                             [Ilustración]\\n']"
      ],
      "metadata": {
        "id": "Z_YPdioTTWjo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8fa7d4ab-0a4d-4eb2-c378-1e807f28496d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6,100 renglones en el archivo: 'data/Los_Caudillos_de_1830.txt'\n",
            "['                              PÍO BAROJA\\n', '\\n', '\\n', '                                  LOS\\n', '                               CAUDILLOS\\n', '                                DE 1830\\n', '\\n', '                                NOVELA\\n', '\\n', '\\n', '                             [Ilustración]\\n']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Limpieza"
      ],
      "metadata": {
        "id": "XgqmK36uJ8ue"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "En muchas ocasiones es necesario limpiar los datos del archivo, por ejemplo en el caso de los tweets a veces es necesario eliminar caracteres especiales como URLs, hashtags, etc.\n",
        "\n",
        "La eliminación de caracteres como los saltos de línea, y los espacios en blanco a la izquierda y derecha del texto son eliminados. De igual manera las líneas del texto vacías deben omitirse en el almacenamiento.\n",
        "\n",
        "Adicionalmente, es clave conservar las oraciones sin saltos de línea que las rompan."
      ],
      "metadata": {
        "id": "HCxWgB07Yrag"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# archivo de texto plano \"Los_Caudillos_de_1830.txt\"\n",
        "# eliminar los saltos de línea y evitar almacenar líneas vacías.\n",
        "\n",
        "ruta_archivo = \"data/Los_Caudillos_de_1830.txt\"\n",
        "datos = []\n",
        "\n",
        "with open(ruta_archivo) as archivo:\n",
        "  for linea in archivo:\n",
        "    # eliminar el caracter de salto de línea y\n",
        "    # eliminar espacios en blanco al inicio y fin del texto de la línea\n",
        "    linea = linea.replace(\"\\n\", \"\").strip()\n",
        "\n",
        "    if linea == \"\":\n",
        "      continue\n",
        "\n",
        "    datos.append(linea)\n",
        "\n",
        "print(f\"{len(datos):,} renglones en el archivo: '{ruta_archivo}'\")\n",
        "# salida esperada:\n",
        "# 4,155 renglones en el archivo: 'data/Los_Caudillos_de_1830.txt'\n",
        "\n",
        "# visualizar los primeros 10 elementos almacenados\n",
        "print(datos[0:11])\n",
        "# salida esperada:\n",
        "# ['PÍO BAROJA', 'LOS', 'CAUDILLOS', 'DE 1830', 'NOVELA', '[Ilustración]', 'RAFAEL CARO RAGGIO: EDITOR',\n",
        "# 'Calle de Ventura Rodríguez, 18', '1918', 'LIBRO PRIMERO', 'EL ETERNO CONSPIRADOR']\n",
        "\n",
        "# visualizar el primer párrafo del libro\n",
        "print(datos[13:17])\n",
        "# salida esperada:\n",
        "# ['UN día, al anochecer, apareció en la fonda de Iturri un hombre que',\n",
        "# 'llamó la atención de Lacy y de Ochoa. Era un tipo seco, amojamado, con',\n",
        "# 'la cara y las manos curtidas por el sol. Tenía el aire de cansancio de',\n",
        "# 'los que vienen de países tropicales.']"
      ],
      "metadata": {
        "id": "M_NikXHZUKMf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "86e0ee1d-77f6-43d5-9cfc-dc6f6a8f925e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4,155 renglones en el archivo: 'data/Los_Caudillos_de_1830.txt'\n",
            "['PÍO BAROJA', 'LOS', 'CAUDILLOS', 'DE 1830', 'NOVELA', '[Ilustración]', 'RAFAEL CARO RAGGIO: EDITOR', 'Calle de Ventura Rodríguez, 18', '1918', 'LIBRO PRIMERO', 'EL ETERNO CONSPIRADOR']\n",
            "['UN día, al anochecer, apareció en la fonda de Iturri un hombre que', 'llamó la atención de Lacy y de Ochoa. Era un tipo seco, amojamado, con', 'la cara y las manos curtidas por el sol. Tenía el aire de cansancio de', 'los que vienen de países tropicales.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# archivo de texto plano \"Los_Caudillos_de_1830.txt\"\n",
        "# eliminar los saltos de línea y evitar almacenar líneas vacías.\n",
        "# conservar los parrafos sin romper las oraciones.\n",
        "\n",
        "ruta_archivo = \"data/Los_Caudillos_de_1830.txt\"\n",
        "datos = []\n",
        "\n",
        "parrafo = \"\"\n",
        "simbolos_terminales = [\".\", \",\", \":\", \";\", \"?\", \"!\"]\n",
        "\n",
        "with open(ruta_archivo) as archivo:\n",
        "  for i, linea in enumerate(archivo):\n",
        "    # eliminar el caracter de salto de línea y\n",
        "    # eliminar espacios en blanco al inicio y fin del texto de la línea\n",
        "    linea = linea.replace(\"\\n\", \"\").strip()\n",
        "\n",
        "    if linea == \"[Ilustración]\":\n",
        "      continue\n",
        "\n",
        "    if len(linea) > 1 and len(parrafo)>=0:\n",
        "      parrafo = f\"{parrafo} {linea.strip()}\"\n",
        "    elif len(linea) == 0 and len(parrafo) > 1:\n",
        "      if parrafo[-1] not in simbolos_terminales:\n",
        "        parrafo += \".\"\n",
        "      datos.append(parrafo.strip())\n",
        "      parrafo = \"\"\n",
        "\n",
        "\n",
        "\n",
        "print(f\"{len(datos):,} renglones en el archivo: '{ruta_archivo}'\")\n",
        "# salida esperada:\n",
        "# 1,747 renglones en el archivo: 'data/Los_Caudillos_de_1830.txt'\n",
        "\n",
        "print(datos[8])\n",
        "# salida esperada:\n",
        "# UN día, al anochecer, apareció en la fonda de Iturri un hombre que llamó la atención de Lacy y de Ochoa. Era un tipo seco, amojamado, con la cara y las manos curtidas por el sol. Tenía el aire de cansancio de los que vienen de países tropicales."
      ],
      "metadata": {
        "id": "z5bnUkVAC0ZW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "369afa1d-8d1b-4223-f4de-e7260f0cb1fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1,747 renglones en el archivo: 'data/Los_Caudillos_de_1830.txt'\n",
            "UN día, al anochecer, apareció en la fonda de Iturri un hombre que llamó la atención de Lacy y de Ochoa. Era un tipo seco, amojamado, con la cara y las manos curtidas por el sol. Tenía el aire de cansancio de los que vienen de países tropicales.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# guardar los datos del archivo en formato csv\n",
        "# utilizar una columna llamada \"texto\"\n",
        "# import pandas as pd\n",
        "\n",
        "\n",
        "#df = pd.DataFrame({\"texto\":datos})\n",
        "\n",
        "#df.to_csv(\"data/Los_Caudillos_de_1830.csv\", index=False)"
      ],
      "metadata": {
        "id": "QmqqT8oC6jCH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Palabras vacías\n",
        "\n",
        "Las **palabras vacías**, también conocidas como **stop words**, son un conjunto de palabras comunes que carecen de significado intrínseco o aportan poca información relevante al contexto de un texto. Son palabras como \"de\", \"la\", \"los\", \"un\", \"una\", \"y\", \"que\", etc.\n",
        "\n",
        "En el PLN, las palabras vacías se suelen eliminar del texto antes de realizar análisis lingüísticos, como la extracción de palabras clave, la clasificación de documentos o la traducción automática. Esto se debe a que estas palabras no contribuyen significativamente al significado general del texto y pueden incluso dificultar el proceso de análisis.\n",
        "\n",
        "**¿Por qué se utilizan las palabras vacías?**\n",
        "\n",
        "Existen varias razones por las que se utilizan las palabras vacías en el PLN:\n",
        "\n",
        "* **Reducir la dimensión del texto**: Eliminar las palabras vacías reduce la cantidad de datos que se deben procesar, lo que puede mejorar la eficiencia de los algoritmos de PLN.\n",
        "* **Mejorar la precisión del análisis**: Al eliminar las palabras vacías, se centran los algoritmos en las palabras que son más importantes para el significado del texto, lo que puede mejorar la precisión del análisis.\n",
        "* **Reducir el ruido**: Las palabras vacías pueden introducir ruido en los datos, lo que puede dificultar la identificación de patrones y relaciones significativas.\n",
        "\n",
        "Sintaxis para eliminar palabras vacías de un texto con `spacy`:\n",
        "\n",
        "```python\n",
        "def eliminar_stopwords(texto):\n",
        "  doc = nlp(texto, disable=[\"ner\"])\n",
        "  tokens_sin_stopwords = [token.text for token in doc if not token.is_stop]\n",
        "  texto_sin_stopwords = \" \".join(tokens_sin_stopwords)\n",
        "  return texto_sin_stopwords\n",
        "\n",
        "texto_original = \"Este es un ejemplo de texto con palabras vacías.\"\n",
        "texto_sin_stopwords = eliminar_stopwords(texto_original)\n",
        "print(f\"Texto original: {texto_original}\")\n",
        "print(f\"Texto sin palabras vacías: {texto_sin_stopwords}\")\n",
        "\n",
        "# salida esperada:\n",
        "# Texto original: Este es un ejemplo de texto con palabras vacías.\n",
        "# Texto sin palabras vacías: ejemplo texto palabras vacías .\n",
        "```"
      ],
      "metadata": {
        "id": "nABlg0iY0CH5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# otra forma de eliminar las palabras vacías de un texto es utilizar el conjunto STOP_WORDS de spacy\n",
        "# from spacy.lang.es.stop_words import STOP_WORDS\n",
        "\n",
        "texto = \"Este es un ejemplo de texto con palabras vacías.\"\n",
        "doc = nlp(texto, disable=[\"ner\"])\n",
        "\n",
        "tokens = []\n",
        "for token in doc:\n",
        "  if token.text.lower() not in STOP_WORDS:\n",
        "    tokens.append(token.text)\n",
        "\n",
        "print(f\"Texto original: {texto}\")\n",
        "print(f\"Texto sin palabras vacías: {tokens}\")\n",
        "\n",
        "# salida esperada:\n",
        "# Texto original: Este es un ejemplo de texto con palabras vacías.\n",
        "# Texto sin palabras vacías: ['ejemplo', 'texto', 'palabras', 'vacías', '.']\n",
        "\n",
        "print(STOP_WORDS)\n",
        "# slida esperada:\n",
        "# {'explicó', 'tendrán', 'nuestros', 'podemos', 'tan', 'después', 'contigo', 'una', ... }"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f1iTxvMu17N_",
        "outputId": "5f9f8752-33c8-46d6-cfff-18df0366f8ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Texto original: Este es un ejemplo de texto con palabras vacías.\n",
            "Texto sin palabras vacías: ['ejemplo', 'texto', 'palabras', 'vacías', '.']\n",
            "{'explicó', 'tendrán', 'nuestros', 'podemos', 'tan', 'después', 'contigo', 'una', 'algo', 'diferente', 'hace', 'mucho', 'propio', 'aún', 'míos', 'un', 'través', 'tenía', 'varios', 'estais', 'sola', 'menudo', 'tercera', 'ustedes', 'ocho', 'mismo', 'últimas', 'misma', 'esos', 'han', 'eramos', 'es', 'mias', 'creo', 'mismas', 'despues', 'las', 'pero', 'somos', 'habla', 'sólo', 'ninguna', 'propia', 'propias', 'durante', 'supuesto', 'seis', 'poder', 'tuyos', 'anterior', 'nuestras', 'serán', 'te', 'puede', 'dado', 'estas', 'propios', 'dejó', 'primeros', 'aproximadamente', 'tras', 'hicieron', 'otras', 'verdad', 'ademas', 'ha', 'estados', 'tambien', 'indicó', 'sabeis', 'además', 'último', 'mios', 'pasado', 'dice', 'haciendo', 'era', 'siete', 'hablan', 'e', 'cada', 'sigue', 'hay', 'muchos', 'poco', 'diferentes', 'tenido', 'aquellos', 'que', 'cuando', 'peor', 'quién', 'podriamos', 'aquí', 'tenga', 'veces', 'arriba', 'apenas', 'yo', 'manera', 'aquello', 'toda', 'tarde', 'en', 'vuestra', 'parte', 'ningún', 'algún', 'aqui', 'donde', 'consigues', 'aquellas', 'embargo', 'vamos', 'trata', 'nadie', 'ningunos', 'usar', 'sea', 'haces', 'había', 'de', 'lleva', 'estaban', 'mío', 'tercero', 'hubo', 'expresó', 'decir', 'estan', 'deben', 'encuentra', 'pocos', 'puedo', 'sera', 'me', 'he', 'mal', 'con', 'tus', 'diez', 'tal', 'saber', 'como', 'usas', 'casi', 'éstas', 'otros', 'tuvo', 'alguno', 'pueda', 'haya', 'él', 'hemos', 'éste', 'conseguimos', 'tuyo', 'consigue', 'todas', 'enseguida', 'mismos', 'nada', 'pronto', 'enfrente', 'allí', 'mías', 'ir', 'ésos', 'mia', 'y', 'verdadera', 'ésa', 'ahora', 'buenas', 'incluso', 'pudo', 'estuvo', 'hasta', 'detrás', 'debajo', 'cualquier', 'ultimo', 'solo', 'consiguen', 'señaló', 'ante', 'estar', 'buena', 'ello', 'afirmó', 'suya', 'total', 'da', 'qué', 'cuanto', 'voy', 'ciertos', 'quizas', 'buen', 'sería', 'qeu', 'alrededor', 'según', 'ni', 'consigo', 'mediante', 'existen', 'antes', 'dos', 'próximos', 'al', 'esta', 'son', 'asi', 'delante', 'podrian', 'adelante', 'usan', 'así', 'alguna', 'cuales', 'ésta', 'cuánto', 'también', 'nosotras', 'vais', 'porque', 'cierta', 'segunda', 'dio', 'sabe', 'tanto', 'deprisa', 'sobre', 'usa', 'sé', 'temprano', 'hacerlo', 'vuestro', 'solos', 'repente', 'ambos', 'o', 'acuerdo', 'partir', 'quedó', 'uno', 'hago', 'soy', 'podria', 'fuimos', 'aseguró', 'última', 'nuevas', 'verdadero', 'mía', 'grandes', 'unos', 'bastante', 'se', 'salvo', 'por', 'cuenta', 'no', 'mas', 'mejor', 'podrían', 'quiza', 'esto', 'segundo', 'eso', 'añadió', 'todo', 'mayor', 'siendo', 'el', 'teneis', 'podría', 'más', 'cuantos', 'mucha', 'quiénes', 'esa', 'dia', 'últimos', 'habrá', 'mientras', 'ser', 'igual', 'este', 'nos', 'nueve', 'será', 'otro', 'todos', 'solamente', 'hacer', 'nueva', 'hacemos', 'primera', 'usamos', 'informó', 'dan', 'quizás', 'estado', 'aquéllas', 'demasiado', 'eran', 'habían', 'fin', 'comentó', 'cuáles', 'bajo', 'usted', 'dar', 'medio', 'tu', 'sin', 'tengo', 'cuantas', 'va', 'cuándo', 'mencionó', 'ninguno', 'lo', 'pasada', 'parece', 'sí', 'nosotros', 'realizar', 'cuál', 'menos', 'informo', 'días', 'podrias', 'tuyas', 'breve', 'ése', 'atras', 'estaba', 'estoy', 'estos', 'cómo', 'pocas', 'consideró', 'llegó', 'podeis', 'eras', 'los', 'posible', 'quiere', 'fui', 'tú', 'saben', 'detras', 'doce', 'siguiente', 'tres', 'está', 'hecho', 'quienes', 'suyos', 'bien', 'hacen', 'poca', 'estará', 'grande', 'a', 'realizado', 'dijeron', 'dijo', 'podrán', 'pueden', 'aquella', 'para', 'sois', 'sido', 'aquélla', 'algunas', 'aquél', 'mí', 'nuestro', 'conseguir', 'podrá', 'dónde', 'eres', 'tenemos', 'vuestros', 'largo', 'lado', 'mio', 'usais', 'vuestras', 'despacio', 'van', 'cuatro', 'todavia', 'sabes', 'manifestó', 'entre', 'considera', 'ciertas', 'le', 'unas', 'proximo', 'suyo', 'estamos', 'algunos', 'aquel', 'quizá', 'once', 'cuánta', 'otra', 'tienen', 'os', 'debe', 'ella', 'sus', 'ya', 'sean', 'tuya', 'aunque', 'cinco', 'su', 'del', 'ese', 'dieron', 'están', 'esas', 'gran', 'existe', 'sino', 'ti', 'día', 'uso', 'encima', 'junto', 'nunca', 'llevar', 'dias', 'vaya', 'solas', 'pesar', 'tiene', 'nuevos', 'dicho', 'mis', 'ésas', 'nuestra', 'alli', 'aun', 'vosotros', 'hoy', 'segun', 'tampoco', 'debido', 'dicen', 'agregó', 'pues', 'ellas', 'contra', 'varias', 'conmigo', 'éstos', 'podriais', 'respecto', 'cuántas', 'haber', 'muy', 'suyas', 'ahí', 'ellos', 'modo', 'conocer', 'primero', 'fueron', 'poner', 'todavía', 'la', 'haceis', 'primer', 'vez', 'buenos', 'habia', 'realizó', 'ningunas', 'aquéllos', 'cual', 'excepto', 'cuántos', 'cuanta', 'queremos', 'ver', 'cierto', 'si', 'vosotras', 'dentro', 'fuera', 'demás', 'bueno', 'ahi', 'paìs', 'fue', 'siempre', 'nuevo', 'u', 'luego', 'desde', 'sabemos', 'mi', 'claro', 'les', 'hacia', 'quien', 'entonces', 'hizo', 'final', 'próximo', 'tener', 'muchas', 'tendrá'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Normalización el texto"
      ],
      "metadata": {
        "id": "ENimHEuTYf2A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "En el Procesamiento de Lenguaje Natural (PLN) el proceso de normalización del texto consiste en la tokenización, lematización y la extracción de etiquetas POS\n"
      ],
      "metadata": {
        "id": "ED77ZIQ5Z13M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Tokenizacion\n",
        "\n",
        "En el ámbito del PLN, un **token** se refiere a la unidad básica de texto que se utiliza para el análisis. Es decir, son segmentos de texto que se obtienen al dividir un texto en unidades más pequeñas y significativas."
      ],
      "metadata": {
        "id": "eJgY5flQbrjC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Objetivos de la tokenización:\n",
        "\n",
        "* **Simplificar el texto**: Al dividir el texto en unidades más pequeñas, se facilita su análisis y procesamiento por parte de las máquinas.\n",
        "* **Identificar unidades lingüísticas**: La tokenización permite identificar unidades lingüísticas relevantes como palabras, frases, números, símbolos, etc.\n",
        "* **Preparar el texto para tareas posteriores**: La tokenización es un paso previo necesario para tareas como el análisis sintáctico, el análisis semántico, la traducción automática, la extracción de información, etc."
      ],
      "metadata": {
        "id": "6GeMvqaFb0HA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Tipos de tokenizacion\n",
        "\n",
        "* **Tokenización a nivel de palabra**: Es la más común y divide el texto en palabras individuales. Por ejemplo, la oración \"La casa roja está en la esquina\" se tokenizaría como: [\"La\", \"casa\", \"roja\", \"está\", \"en\", \"la\", \"esquina\"].\n",
        "* **Tokenización a nivel de subpalabra**: Divide las palabras en sus componentes más pequeños, como prefijos, sufijos y raíces. Por ejemplo, la palabra \"comprender\" se tokenizaría como: [\"com\", \"prender\"].\n",
        "* **Tokenización a nivel de carácter**: Divide el texto en caracteres individuales. Por ejemplo, la oración \"Hola mundo\" se tokenizaría como: [\"H\", \"o\", \"l\", \"a\", \" \", \"m\", \"u\", \"n\", \"d\", \"o\"]."
      ],
      "metadata": {
        "id": "XKA4DwNNb13-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Tokenización con `spacy`\n",
        "\n",
        "Para realizar el proceso de tokenizacion se utiliza la biblioteca `spacy`. A continuación se presenta la sintaxis básica:\n",
        "\n",
        "```python\n",
        "# importar la biblioteca\n",
        "import spacy\n",
        "\n",
        "# cargar el modelo dependiendo del idioma reuqerido para procesar\n",
        "nlp = spacy.load('es_core_news_sm')\n",
        "\n",
        "# texto a tokenizar\n",
        "texto = \"Esta es una oración, que será tokenizada!\"\n",
        "\n",
        "# realizar la tokenización del texto\n",
        "doc = nlp(texto, disable=[\"ner\"])\n",
        "\n",
        "tokens = [token.text for token in doc]\n",
        "print(tokens)\n",
        "```"
      ],
      "metadata": {
        "id": "hqf3pixfc5ZB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# tokenizar el conjunto de datos obtenido del archivo\n",
        "# para tokenizar se utiliza la biblioteca Spacy\n",
        "\n",
        "# el modelo en español que usa Spacy ya se encuentra cargado\n",
        "\n",
        "# texto a tokenizar\n",
        "texto = \"Esta es una oración, que será tokenizada! Hoy es 25-06-2024, estamos en el taller-PLN.\"\n",
        "\n",
        "# realizar la tokenización del texto\n",
        "doc = nlp(texto, disable=[\"ner\"])\n",
        "\n",
        "oraciones = [] # variable para almacenar las listas de tokens por cada oración\n",
        "for sentence in doc.sents: # iterar las oraciones identificadas en el texto\n",
        "  tokens = [token.text for token in sentence] # obtener el texto de los tokens\n",
        "  oraciones.append(tokens) # almacenar la lista de tokens por cada oración\n",
        "\n",
        "# visulizar el texto tokenizado\n",
        "print(oraciones)\n",
        "# salida esperada:\n",
        "# [['Esta', 'es', 'una', 'oración', ',', 'que', 'será', 'tokenizada', '!'],\n",
        "# ['Hoy', 'es', '25-06-2024', ',', 'estamos', 'en', 'el', 'taller-PLN', '.']]\n"
      ],
      "metadata": {
        "id": "sFXHAq_zcJ7a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb084bc1-d9c3-4117-909f-f18ab24d3f37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['Esta', 'es', 'una', 'oración', ',', 'que', 'será', 'tokenizada', '!'], ['Hoy', 'es', '25-06-2024', ',', 'estamos', 'en', 'el', 'taller-PLN', '.']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Lematización"
      ],
      "metadata": {
        "id": "lWo4rzzRfQBM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "La lematización es un proceso que consiste en reducir las palabras a su **forma base** o **lema**, también conocida como **forma canónica**.\n",
        "\n",
        "A diferencia de la stemming, que se basa en reglas heurísticas para eliminar sufijos y prefijos, la lematización utiliza un diccionario y reglas morfológicas para identificar la forma base correcta de una palabra, tomando en cuenta su contexto gramatical y semántico."
      ],
      "metadata": {
        "id": "2XzG48e8fVju"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Objetivos de la lematización:\n",
        "\n",
        "* **Normalizar el texto**: La lematización permite agrupar palabras flexionadas (conjugaciones, declinaciones) en su forma base, lo que reduce la redundancia y facilita el análisis del texto.\n",
        "* **Mejorar la recuperación de información**: Al buscar información en un corpus de texto, la lematización permite encontrar coincidencias incluso si las palabras de búsqueda y los documentos están escritos con diferentes flexiones.\n",
        "* **Reducir la dimensionalidad**: La lematización puede reducir la dimensionalidad de los datos de texto, lo que puede ser útil para tareas como el modelado de temas o la clasificación de textos."
      ],
      "metadata": {
        "id": "A_-Npc0hkepq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Ejemplos de lematización:\n",
        "\n",
        "| Palabra | Flexiones | Lemma |\n",
        "|----------|----------|----------|\n",
        "| comer | como, comes, comemos, coméis, comen   | comer |\n",
        "| jugar | juego, juegas, jugamos, jugáis, juegan\t| jugar |\n",
        "| país | países | país |\n",
        "| ciudad | ciudades | ciudad |"
      ],
      "metadata": {
        "id": "ftkSlp4_khJQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Lematización con `spacy`\n",
        "\n",
        "La lematización con la biblioteca `spacy` consiste de la sintaxis:\n",
        "\n",
        "```python\n",
        "# importar la biblioteca\n",
        "import spacy\n",
        "\n",
        "# cargar el modelo dependiendo del idioma reuqerido para procesar\n",
        "nlp = spacy.load('es_core_news_sm')\n",
        "\n",
        "# texto a lematizar\n",
        "texto = \"Los niños jugaban en el parque.\"\n",
        "\n",
        "# realizar la tokenización del texto\n",
        "doc = nlp(texto, disable=[\"ner\"])\n",
        "\n",
        "lemmas = [token.lemma_ for token in doc]\n",
        "print(lemmas)\n",
        "# salida esperada:\n",
        "# ['el', 'niño', 'jugar', 'en', 'el', 'parque', '.']\n",
        "```"
      ],
      "metadata": {
        "id": "EQP5kUZ_hbBM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# el modelo en español que usa Spacy ya se encuentra cargado\n",
        "\n",
        "# texto a lematizar\n",
        "texto = \"Los niños jugaban en el parque. Las niñas corrían por el jardín.\"\n",
        "\n",
        "# realizar la tokenización del texto\n",
        "doc = nlp(texto, disable=[\"ner\"])\n",
        "\n",
        "for sentence in doc.sents:\n",
        "  print('-'*20)\n",
        "  print(f\"token\\tlemma\")\n",
        "  print('-'*20)\n",
        "  for token in sentence:\n",
        "    print(f\"{token.text}\\t{token.lemma_}\")\n",
        "\n",
        "# salida esperada:\n",
        "# --------------------\n",
        "# token\tlemma\n",
        "# --------------------\n",
        "# Los\tel\n",
        "# niños\tniño\n",
        "# jugaban\tjugar\n",
        "# en\ten\n",
        "# el\tel\n",
        "# parque\tparque\n",
        "# .\t.\n",
        "# --------------------\n",
        "# token\tlemma\n",
        "# --------------------\n",
        "# Las\tel\n",
        "# niñas\tniña\n",
        "# corrían\tcorrer\n",
        "# por\tpor\n",
        "# el\tel\n",
        "# jardín\tjardín\n",
        "# .\t."
      ],
      "metadata": {
        "id": "OQ15Ki03hXlM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f689329b-57ff-4a04-bb16-298c5f56d941"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------\n",
            "token\tlemma\n",
            "--------------------\n",
            "Los\tel\n",
            "niños\tniño\n",
            "jugaban\tjugar\n",
            "en\ten\n",
            "el\tel\n",
            "parque\tparque\n",
            ".\t.\n",
            "--------------------\n",
            "token\tlemma\n",
            "--------------------\n",
            "Las\tel\n",
            "niñas\tniña\n",
            "corrían\tcorrer\n",
            "por\tpor\n",
            "el\tel\n",
            "jardín\tjardín\n",
            ".\t.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Obtener etiquetas POS"
      ],
      "metadata": {
        "id": "Ob-Z2V9kkGXW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "El etiquetado de partes del discurso (POS) para texto en español, es la tarea de asignar a cada palabra en un texto una etiqueta que indica su categoría gramatical, como sustantivo, verbo, adjetivo, etc.\n",
        "\n",
        "Para conocer más sobre las etiquetas POS consultar [Universal POS tags](https://universaldependencies.org/docs/u/pos/).\n",
        "\n",
        "La sintaxis básica empleando `spacy`es:\n",
        "\n",
        "```python\n",
        "import spacy\n",
        "\n",
        "# Cargar el modelo de español\n",
        "nlp = spacy.load(\"es_core_web_sm\")\n",
        "\n",
        "# texto a procesar\n",
        "texto = \"La casa azul está en la colina. El auto rojo sube la colina lentamente.\"\n",
        "\n",
        "# Procesar el texto con el modelo\n",
        "doc = nlp(texto)\n",
        "\n",
        "# Recorrer las palabras del documento\n",
        "for token in doc:\n",
        "    # Obtener la etiqueta POS\n",
        "    pos_tag = token.pos_\n",
        "\n",
        "    # Imprimir la palabra y su etiqueta POS\n",
        "    print(f\"{token} - {pos_tag}\")\n",
        "\n",
        "```"
      ],
      "metadata": {
        "id": "ZkGtSqjMkL4W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# texto a procesar\n",
        "texto = \"La casa azul está en la colina. El auto rojo sube la colina lentamente.\"\n",
        "\n",
        "# Procesar el texto con el modelo\n",
        "doc = nlp(texto, disable=[\"ner\"])\n",
        "\n",
        "# Recorrer las palabras del documento\n",
        "for token in doc:\n",
        "\n",
        "    # Obtener la etiqueta POS\n",
        "    pos_tag = token.pos_\n",
        "\n",
        "    # Imprimir la palabra y su etiqueta POS\n",
        "    print(f\"{token}\\t\\t{pos_tag}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "57Au-sqik_Kf",
        "outputId": "ca8df149-d639-480a-e000-d87aa08c3be8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "La\t\tDET\n",
            "casa\t\tNOUN\n",
            "azul\t\tADJ\n",
            "está\t\tVERB\n",
            "en\t\tADP\n",
            "la\t\tDET\n",
            "colina\t\tNOUN\n",
            ".\t\tPUNCT\n",
            "El\t\tDET\n",
            "auto\t\tNOUN\n",
            "rojo\t\tADJ\n",
            "sube\t\tVERB\n",
            "la\t\tDET\n",
            "colina\t\tNOUN\n",
            "lentamente\t\tADV\n",
            ".\t\tPUNCT\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Análisis de dependencias"
      ],
      "metadata": {
        "id": "-3r4LcNitKkt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Un analizador de dependencias (en inglés, dependency parser) es una herramienta fundamental en el PLN que se utiliza para analizar la estructura sintáctica de una oración. A diferencia del análisis sintáctico tradicional, que se centra en la jerarquía de las palabras en una oración, el análisis de dependencias se enfoca en las relaciones entre las palabras.\n",
        "\n",
        "En el análisis de dependencias, cada palabra se considera como un nodo en un árbol, y las relaciones entre las palabras se representan como arcos que conectan los nodos. El tipo de arco indica la relación específica entre dos palabras, como por ejemplo:\n",
        "\n",
        "* **Sujeto**: El nodo que realiza la acción del verbo.\n",
        "* **Objeto directo**: El nodo que recibe la acción del verbo.\n",
        "* **Objeto indirecto**: El nodo al que se beneficia o perjudica la acción del verbo.\n",
        "* **Adjetivo**: Un nodo que modifica a un sustantivo.\n",
        "* **Adverbio**: Un nodo que modifica a un verbo, adjetivo o otro adverbio.\n",
        "\n",
        "Los analizadores de dependencias se utilizan en una amplia variedad de aplicaciones de PLN, como:\n",
        "\n",
        "* **Traducción automática**: Para comprender mejor la estructura de una oración en un idioma y traducirla con mayor precisión a otro idioma.\n",
        "* **Recuperación de información**: Para identificar y extraer información relevante de documentos de texto.\n",
        "* **Respuesta a preguntas**: Para comprender el significado de una pregunta y generar una respuesta precisa.\n",
        "* **Resumen de texto**: Para identificar las ideas principales de un texto y generar un resumen conciso.\n",
        "* **Análisis de sentimientos**: Para determinar el sentimiento o la opinión expresada en un texto.\n",
        "\n",
        "Para cononcer más sobre las **dependencias** en español [Universal Dependencies](https://universaldependencies.org/es/dep/index.html)\n"
      ],
      "metadata": {
        "id": "78GxZQhstcJQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from graphviz import Digraph\n",
        "\n",
        "text = \"Beatriz Gutiérrez es la esposa de AMLO.\"\n",
        "doc = nlp(text, disable=[\"ner\"])\n",
        "\n",
        "def create_dependency_tree(doc):\n",
        "    dot = Digraph()\n",
        "    for token in doc:\n",
        "        dot.node(str(token.i), f\"{token.text}\\n({token.pos_})\")\n",
        "        if token.head.i != token.i:  # token.head.i == token.i if the token is the root\n",
        "            dot.edge(str(token.head.i), str(token.i), label=token.dep_)\n",
        "    return dot\n",
        "\n",
        "dot = create_dependency_tree(doc)\n",
        "dot.render(\"dependency_tree\", format=\"png\")\n",
        "\n",
        "Image(filename=\"dependency_tree.png\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "id": "Dt5jGyY-uE78",
        "outputId": "646857a0-18d2-4afc-a49b-c78354528df3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"Sheinbaum ex jefa de Gobierno de la Ciudad de México.\"\n",
        "doc = nlp(text, disable=[\"ner\"])\n",
        "\n",
        "dot = create_dependency_tree(doc)\n",
        "dot.render(\"dependency_tree\", format=\"png\")\n",
        "\n",
        "Image(filename=\"dependency_tree.png\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "xxXyLwrQweW0",
        "outputId": "db190acf-62e6-4d33-a8b7-2f4ed92b5408"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    }
  ]
}