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
Aprendizaje autónomo
5.3
Competencias
Competencias Técnicas Genéricas
Genéricas
CG3 - Capacidad para la modelización, cálculo, simulación, desarrollo e implantación en centros tecnológicos y de ingeniería de empresa, particularmente en tareas de investigación, desarrollo e innovación en todos los ámbitos relacionados con la Inteligencia Artificial.
Competencias Técnicas de cada especialidad
Académicas
CEA3 - Capacidad de comprender los principios básicos de funcionamiento de las técnicas principales de Aprendizaje Automático, y saber utilizarlas en el entorno de un sistema o servicio inteligente.
CEA5 - Capacidad de comprender los principios básicos de funcionamiento de las técnicas de Procesamiento del Lenguaje Natural, y saber utilizarlas en el entorno de un sistema o servicio inteligente.
Competencias Transversales
Trabajo en equipo
CT3 - Ser capaz de trabajar como miembro de un equipo interdisciplinar ya sea como un miembro mas, o realizando tareas de direccion con la finalidad de contribuir a desarrollar proyectos con pragmatismo y sentido de la responsabilidad, asumiendo compromisos teniendo en cuenta los recursos disponibles.
Razonamiento
CT6 - Capacidad de evaluar y analizar de manera razonada y critica sobre situaciones, proyectos, propuestas, informes y estudios de caracter cientifico-tecnico. Capacidad de argumentar las razones que explican o justifican tales situaciones, propuestas, etc.
Analisis y sintesis
CT7 - Capacidad de analisis y resolucion de problemas tecnicos complejos.
Básicas
CB6 - Que los estudiantes sepan aplicar los conocimientos adquiridos y su capacidad de resolución de problemas en entornos nuevos o poco conocidos dentro de contextos más amplios (o multidisciplinares) relacionados con su área de estudio.
CB8 - Que los estudiantes sepan comunicar sus conclusiones y los conocimientos y razones últimas que las sustentan a públicos especializados y no especializados de un modo claro y sin ambigüedades.
CB9 - Que los estudiantes posean las habilidades de aprendizaje que les permitan continuar estudiando de un modo que habrá de ser en gran medida autodirigido o autónomo.
Objetivos
Learn to apply statistical methods for NLP in a practical application
Competencias relacionadas:
CEA3,
CEA5,
CT3,
CB6,
CB8,
CB9,
Understand statistical and machine learning techniques applied to NLP
Competencias relacionadas:
CEA3,
CG3,
CT6,
CT7,
CB6,
Develop the ability to solve technical problems related to statistical and algorithmic problems in NLP
Competencias relacionadas:
CEA3,
CEA5,
CG3,
CT7,
CB6,
CB8,
CB9,
Understand fundamental methods of Natural Language Processing from a computational perspective
Competencias relacionadas:
CEA5,
CT7,
CB6,
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.
Actividades
ActividadActo evaluativo
Course Introduction
Review of the field of Natural Language Processing, and the main challenges in the field. Review of the statistical paradigm. Review of language models. The student has to understand the basic questions for which we will see a variety of techniques during the course. Objetivos:42 Contenidos:
These lectures present machine learning algorithms used in the field of NLP. Special attention is given to the difference between generative and discriminative methods for parameter estimation. We will also present the type of features that are typically used in NLP in discriminative methods. We expect that students already have some background in machine learning, and the goal of these lectures is to see how machine learning is applied to NLP. Objetivos:42 Contenidos:
These lectures will present sequence models, an important set of tools that is used for sequential tasks. We will present this in the framework of structured prediction (later in the course we will see that the same framework is used for parsing and translation). We will focus on machine learning aspects, as well as algorithmic aspects. We will give special emphasis to Conditional Random Fields. Objetivos:42 Contenidos:
We will present statistical models for syntactic structure, and in general tree structures. The focus will be on probabilistic context-free grammars and dependency grammars, two standard formalisms. We will see relevant algorithms, as well as methods to learn grammars from data based on the structured prediction framework. Objetivos:42 Contenidos:
We will present the basic elements of statistical machine translation systems, including representation aspects, algorithmic aspects, and methods for parameter estimation. Objetivos:42 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. Objetivos:42 Contenidos:
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.