Can a machine learn to correct the grammaticality of text? Can a machine learn to answer questions we make in plain English? Can a machine learn to translate languages, using Wikipedia as a training set?
This course offers an in depth coverage of methods for Natural Language Processing. We will present fundamental models and tools to approach a variety of Natural Language Processing tasks, ranging from syntactic processing, to semantic processing, to final applications such as information extraction, human-machine dialogue systems, and machine translation. The flow of the course is along two main axis: (1) computational formalisms to describe natural language processes, and (2) statistical and machine learning methods to acquire linguistic models from large data collections.
Horas semanales
Teoría
2
Problemas
1
Laboratorio
0
Aprendizaje dirigido
0.6
Aprendizaje autónomo
6.5
Objetivos
Understand fundamental methods of Natural Language Processing from a computational perspective
Competencias relacionadas:
CG3,
CB6,
CB9,
CEC1,
CEC2,
CTR6,
Understand statistical and machine learning techniques applied to NLP
Competencias relacionadas:
CG3,
CB6,
CB9,
CEC1,
CEC2,
CTR6,
Develop the ability to solve technical problems related to statistical and algorithmic problems in NLP
Competencias relacionadas:
CG3,
CB6,
CB8,
CB9,
CEC1,
CEC2,
CTR6,
Learn to apply statistical methods for NLP in a practical application
Competencias relacionadas:
CG3,
CB6,
CB8,
CB9,
CEC1,
CEC2,
CTR3,
CTR6,
Contenidos
Course Introduction
Fundamental tasks in NLP. Main challenges in NLP. Review of statistical paradigms. Review of language modeling techniques.
Classification in NLP
Review of supervised machine learning methods. Linear classifiers. Generative and discriminative learning. Feature representations in NLP. The EM algorithm.
Sequence Models
Hidden Markov Models. Log-linear models and Conditional Random Fields. Applications to part-of-speech tagging and named-entity extraction.
Syntax and Parsing
Probabilistic Context Free Grammars. Dependency Grammars. Parsing Algorithms. Discriminative Learning for Parsing.
Machine Translation
Introduction to Statistical Machine Translation. The IBM models. Phrase-based methods. Syntax-based approaches to translation.
Unsupervised and Semisupervised methods in NLP
Bootstrapping. Cotraining. Distributional methods.
We will present the basic elements of statistical machine translation systems, including representation aspects, algorithmic aspects, and methods for parameter estimation. Objetivos:12 Contenidos:
We will review several methods for unsupervised learning in NLP, in the context of lexical models, sequence models, and grammatical models. We will focus on bootstrapping and cotraining methods, the EM algorithm, and distributional methods
The course will be structured around five main blocks of lectures. In each theory lecture, we will present fundamental algorithmic and statistical techniques for NLP. This will be followed by problem lectures, where we will look in detail to derivations of algorithms and mathematical proofs that are necessary in order to understand statistical methods in NLP.
Furthermore, there will be four problem sets that students need to solve at home. Each problem set will consist of three or four problems that will require the student to understand the elements behind statistical NLP methods. In some cases these problems will involve writing small programs to analyze data and perform some computation.
Finally, students will develop a practical project in teams of two or three students. The goal of the project is to put into practice the methods learned in class, and learn how the experimental methodology that is used in the NLP field. Students have to identify existing components (i.e. data and tools) that can be used to build a system, and perform experiments in order to perform empirical analysis of some statistical NLP method.