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