1
0
mirror of https://github.com/gsi-upm/sitc synced 2024-11-14 02:32:27 +00:00
sitc/nlp/4_0_Intro_NLP.ipynb
2016-05-26 14:33:27 +02:00

93 lines
2.3 KiB
Plaintext

{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](images/EscUpmPolit_p.gif \"UPM\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Course Notes for Learning Intelligent Systems"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to Natural Language Processing\n",
" \n",
"In this lab session, we are going to learn how to analyze texts and apply machine learning techniques on textual data sources.\n",
"\n",
"# Objectives\n",
"\n",
"The main objectives of this session are:\n",
"* Learn how to obtain lexical, syntactic and semantic features from texts\n",
"* Learn to use some libraries, such as NLTK, Scikit-learn and gensim for NLP"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Table of Contents"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. [Home](4_0_Intro_NLP.ipynb)\n",
"1. [Lexical processing](4_1_Lexical_Processing.ipynb)\n",
"1. [Syntactic processing](4_2_Syntactic_Processing.ipynb)\n",
"2. [Vector representation](4_3_Vector_Representation.ipynb)\n",
"3. [Classification](4_4_Classification.ipynb)\n",
"1. [Semantic models](4_5_Semantic_Models.ipynb)\n",
"1. [Combining features](4_6_Combining_Features.ipynb)\n",
"5. [Exercises](4_7_Exercises.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Licence\n",
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
"\n",
"© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}