Tecnologías Avanzadas del Lenguaje Humano

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Créditos
5
Tipos
  • MIRI: Complementaria de especialidad (Ciencia de los Datos)
  • MAI: Optativa
Requisitos
Esta asignatura no tiene requisitos, pero tiene capacidades previas
Departamento
CS;TSC
Web
http://www.lsi.upc.edu/~padro/ahlt/ahlt.html
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.

Profesores

Responsable

  • Lluis Padro Cirera ( )

Horas semanales

Teoría
2
Problemas
0
Laboratorio
1
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

  1. Learn to apply statistical methods for NLP in a practical application
    Competencias relacionadas: CEA3, CEA5, CT3, CB6, CB8,
  2. Understand statistical and machine learning techniques applied to NLP
    Competencias relacionadas: CEA3, CG3, CT6, CT7, CB6,
  3. Develop the ability to solve technical problems related to statistical and algorithmic problems in NLP
    Competencias relacionadas: CEA3, CEA5, CG3, CT7, CB6, CB8, CB9,
  4. Understand fundamental methods of Natural Language Processing from a computational perspective
    Competencias relacionadas: CEA5, CT7, CB6,

Contenidos

  1. Syntactic Parsing
    Three lectures of the course will be devoted to syntactic parsing:

    1.- Statistical parsing. The core are SCFG. Learning (supervised from treebanks or unsupervised using the inside/outside algorithm), parsing (Viterbi). Pros & Cons of SCFG. Other probabilistic approaches.

    2.- Dependency Parsing. Projective and non projective dependency trees. Eisner & Chu, Liu, Edmonds algorithms. Transition-based parsing.

    3.- Robust parsing. Chunking. HMM-based chunkers. Cascaded FSM chunkers, grammars for chunking.
  2. Distances and Similarities
    Distances (and similarities) between linguistic units. Textual, Semantic, and Distributional distances. Semantic spaces (WN, Wikipedia, Freebase, Dbpedia).
  3. Semantic Role Labelling
    The concept of semantic role. Mapping syntactic dependencies into semantic roles. Semantic arguments of a predicate. Semantic Role Labelers. Resources for learning SRL: VerbNet, PropBank.
  4. Semantic Parsing
    Semantic Representation. Semantic parsing. Building semantic grammars. Learning semantic parsers.
  5. Distributional models
    Distributional models of semantics. Vector Space Model (VSM). Dimensionality reduction. Latent Semantic Indexing (LSI). Using Topic models: Latent Dirichlet Allocation (LDA).
  6. Linguistic Inference
    Detecting inference between linguistic units. Recognizing Textual Entailment. The case of paraphrasing.
  7. Deep Learning for NLP
    Three lectures will be devoted to Deep Learning for NLP

    1.- Using standard Python modules for ML approaches to NLP tasks: Scipy, sklearn for basic ML models. Neural Networks. Linear models. Feed Forward NN. Simple Perceptron. Multilayer Perceptron (MLP). NLP applications.

    2.- Libraries and languages for NN: Theano, TensorFlow, Keras. More advanced NN. Convolutional NN, Embeddings of words and more complex units. Word2Vec and other embeddings. NLP applications.

    3.- Recurrent NN (RNN), Combination of RNN, NN with memory: GRU, LSTM. NLP applications.

Actividades

Actividad Acto 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: 4 2
Teoría
2h
Problemas
1h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h

Classification in NLP

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: 4 2
Contenidos:
Teoría
5h
Problemas
3h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h

Problem Set 1


Objetivos: 4 2 3
Semana: 4
Tipo: entrega
Teoría
0h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
6h

Sequence Models in NLP

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: 4 2
Contenidos:
Teoría
6h
Problemas
4h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h

Problem Set 2


Objetivos: 4 2 3
Semana: 7
Tipo: entrega
Teoría
0h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
6h

Syntax and Parsing

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: 4 2
Contenidos:
Teoría
6h
Problemas
3h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h

Problem Set 3


Objetivos: 4 2 3
Semana: 10
Tipo: entrega
Teoría
0h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
6h

Statistical Machine Translation

We will present the basic elements of statistical machine translation systems, including representation aspects, algorithmic aspects, and methods for parameter estimation.
Objetivos: 4 2
Contenidos:
Teoría
4h
Problemas
2h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h

Unsupervised Methods in NLP

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: 4 2
Contenidos:
Teoría
4h
Problemas
2h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h

Problem Set 4


Objetivos: 4 2 3
Semana: 14
Tipo: entrega
Teoría
0h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
6h

Final Exam


Objetivos: 4 2 3
Semana: 15
Tipo: examen de teoría
Teoría
3h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
10h

Project


Objetivos: 4 2 1
Semana: 16
Tipo: entrega
Teoría
0h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
45h

Metodología docente

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.

Método de evaluación

Final grade = 0.5*FE + 0.5*LP

where

FE is the grade of the final exam

LP is the grade of the lab project

Bibliografía

Básica:

Web links

Capacidades previas

- Introductory concepts and methods of Natural Language processing.

- Introductory concepts and methods of Machine Learning.

- Programming.