Natural Languages

Facultade de Informática da Coruña
Computer Science Engineering

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REMARK: there exists a official page of the course in the web site of the Faculty.

IMPORTANT REMARK: Information provided in this pages does not substitute the official information published in official media.


Rooms and Timeline


  1.  Introduction
    1. Levels of analysis
    2. Ambiguity
  2.  Linguistic Resources
    1. Tag-sets
    2. Dictionaries
    3. Tagged texts
    4. Tree-banks
  3. Lexical Analysis
    1. Text segmentation
    2. Flexive and derivative morphology
    3. Modelizing large dictionaries
    4. Numbered acyclic deterministic finite-state automata
    5. Finite-state transducers and two-level morphology
  4. Tagging
    1. Hidden Markov Models
    2. Efficient execution of Hidden Markov Models
    3. Smoothing techniques
    4. Dealing with unknown words
    5. Transformation-based and error-driven tag learning
  5. Context-free parsing
    1. Parsing schemata
    2. Bottom-up parsing
    3. Earley's parser
    4. Push-down automata and dynamic programming
    5. Generalized LR parsers
    6. Shared forest
    7. Probabilistic parsing
  6. Parsing of mildly context-sensitive languages
    1. Tree adjoining grammars
    2. Parsing tree adjoining grammars
    3. Automata for parsing tree adjoining grammars
    4. Derivation trees
    5. Probabilistic Representación compartida de los árboles de derivación
  7. Semantic analysis
    1. Feature structures and unification-based formalisms
    2. Lexical relations: WordNet and EuroWordNet
  8. Information Retrieval (IR)
    1. Basic concepts
    2. Retrieval models: boolean, vector and probabilistic
    3. Indexing and retrieval
    4. Evaluation of IR systems
    5. Wen IR. A case in point: Google
    6. Applications of natural language processing to IR: linguistic variation
  9. Information Extraction (IE)
    1. Basic concepts
    2. Arquitecture of an IE system
    3. IE tasks
    4. Evaluation of IE systema
    5. Examples of IE sytems: FASTUS and others
  10. Question Answering (QA)
    1. Basic conceptos
    2. QA vs. IR/IE
    3. Arquitecture of a QA syetem
    4. Question processing
    5. Retrieving and selectinf documents/passages
    6. Answer extraction
    7. Evaluaction of QA systems
  11. Machine Translation (MT)
    1. Basic concepts and open issues
    2. "Classic" approaches
    3. Statistical approaches
    4. Applications in multilingual IR

Basic Bibliography

Additional Bibliography:

In shelves I28 of the librtary you can found a lot of books on Natural Language Processing. We strongly recommend to visit that part of the library.


Lecture notes:

Student time

See the web page of the Faculty

Practical works



Last modified: Mon Oct 19 12:23:59 CEST 2010