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• Presentación de la materia.
• Introducción al Procesamiento de Lenguaje Natural.
  • https://www.youtube.com/watch?v=nfoudtpBV68&index=1&list=PL6397E4B26D00A269
• Procesamiento básico de texto: Expresiones regulares, tokenización, segmentación, normalización, lematización, stemming, etc.
  • https://www.youtube.com/watch?v=hwDhO1GLb_4&index=2&list=PL6397E4B26D00A269
  • https://www.youtube.com/watch?v=RGLldper5II&list=PL6397E4B26D00A269&index=3
  • https://www.youtube.com/watch?v=jBk24DI8kg0&index=4&list=PL6397E4B26D00A269
  • https://www.youtube.com/watch?v=2s7f8mBwnko&list=PL6397E4B26D00A269&index=5

• Procesamiento básico de texto: segmentación de oraciones.
  • https://www.youtube.com/watch?v=di0N3kXfGYg&index=6&list=PL6397E4B26D00A269&t=4s
• Modelado de Lenguaje: N-gramas.
  • https://www.youtube.com/watch?v=s3kKlUBa3b0&list=PL6397E4B26D00A269&index=12
  • https://www.youtube.com/watch?v=o-CvoOkVrnY&list=PL6397E4B26D00A269&index=13
• Modelado de Lenguaje: Evaluación (log-probability, perplexity). (curso de Collins)
  • https://www.youtube.com/watch?v=NlmKb0X-nkA&list=PLGQnlmH5d2z6ehl1bTY8BQxDTCHfr1TSY&index=8

• Modelado de Lenguaje: Suavizado (smoothing).
  • https://www.youtube.com/watch?v=s5Yg6qac9ag&list=PL6397E4B26D00A269&index=15
  • https://www.youtube.com/watch?v=d8nVJjlMOYo&list=PL6397E4B26D00A269&index=16
  • https://www.youtube.com/watch?v=-aMYz1tMfPg&index=17&list=PL6397E4B26D00A269
  • https://www.youtube.com/watch?v=XdjCCkFUBKU&list=PL6397E4B26D00A269&index=18
• Modelado de Lenguaje: Back-Off con discounting. (curso de Collins)
  • https://www.youtube.com/watch?v=hsHw9F3UuAQ&list=PLGQnlmH5d2z6ehl1bTY8BQxDTCHfr1TSY&index=11
  • https://www.youtube.com/watch?v=FedWcgXcp8w&index=12&list=PLGQnlmH5d2z6ehl1bTY8BQxDTCHfr1TSY
• Notas complementarias:
  • http://www.cs.columbia.edu/~mcollins/lm-spring2013.pdf
  • https://cs.famaf.unc.edu.ar/~francolq/lm-notas.pdf

(13 y 14 semana santa, 20 consulta práctico 1)

• Etiquetado de secuencias: Introducción al Part-of-Speech (POS) tagging.
  • 12 - 1 - An Intro to POS Tagging
• Etiquetado de secuencias: Introducción, modelos generativos, HMMs (curso de Collins).
  • 1. The Tagging Problem
  • 2. Generative Models for Supervised Learning
  • 3. Hidden Markov Models (HMMs) Basic Definitions
  • 4. Parameter Estimation in HMMs

• Etiquetado de secuencias: Algoritmo de Viterbi para HMMs (curso de Collins).
  • 5. The Viterbi Algorithm for HMMs - Part I
  • 6. The Viterbi Algorithm for HMMs - Part II
  • 7. The Viterbi Algorithm for HMMs - Part III
  • 8. Summary

(repetida el 5 de mayo)

• Modelos log-linear:
  • 1. Introduction
  • 2. Two Example Problems
  • 3. Features in Log-Linear Models - Part I
  • 4. Features in Log-Linear Models - Part II
  • 5. Definition of Log-Linear Models - Part I
  • 6. Definition of Log-Linear Models - Part II
  • 7. Parameter Estimation in Log-Linear Models - Part I
  • 8. Parameter Estimation in Log-Linear Models - Part II
  • 9. Smoothing Regularization in Log-Linear Models
• Modelos log-linear para etiquetado de secuencias:
  • 1. Introduction
  • 2. Recap of the Tagging Problem
  • 3. Independence Assumptions in Log-linear Taggers
  • 4. Features in Log-Linear Taggers
  • 5. Parameters in Log-Linear Models
  • 6. The Viterbi Algorithm for Log-Linear Taggers
  • 7. An Example Application
  • 8. Summary

Paro educativo.

• Análisis sintáctico y gramáticas libres de contexto (curso de Collins):
  • 1. Introduction
  • 2. Introduction to the Parsing Problem - Part I
  • 3. Introduction to the Parsing Problem - Part II
  • 4. Context-Free Grammars - Part I
  • 5. Context-Free Grammars - Part II
  • 6. A Simple Grammar for English - Part I
  • 7. A Simple Grammar for English - Part II
  • 8. A Simple Grammar for English - Part III
  • 9. A Simple Grammar for English - Part IV
  • 10. Examples of Ambiguity

• Parsing Introduction (curso de Jurafsky y Manning):
  • 1. Syntactic Structure Constituency vs Dependency
  • 2. Empirical Data-Driven Approach to Parsing
  • 3. The Exponential Problem in Parsing
• PCFGs (curso de Collins):
  • 1. Introduction
  • 2. Basics of PCFGs - Part I
  • 3. Basics of PCFGs - Part II
  • 4. The CKY Parsing Algorithm - Part I
  • 5. The CKY Parsing Algorithm - Part II
  • 6. The CKY Parsing Algorithm - Part III

• CKY Example
  • https://www.youtube.com/watch?v=MiEKnFyErbQ&t=283s&index=65&list=PL6397E4B26D00A269
• Constituency Parser Evaluation
  • https://www.youtube.com/watch?v=mMXgbrts82M&index=66&list=PL6397E4B26D00A269
• Weaknesses of PCFGs
  • https://www.youtube.com/watch?v=xgBsLALt8jk&list=PLO9y7hOkmmSFRv6QnqR2qjqDB8pbkZKwX&index=1&t=7s
• Lexicalized PCFGs (curso de Collins):
  • 1. Introduction
  • 2. Lexicalization of a Treebank
  • 3. Lexicalized PCFGs Basic Definitions
  • 4. Parameter Estimation in Lexicalized PCFGs - Part I
  • 5. Parameter Estimation in Lexicalized PCFGs - Part II
  • 6. Evaluation of Lexicalized PCFGs - Part I
  • 7. Evaluation of Lexicalized PCFGs - Part II