Human Language Processing

Credits
6
Types
Compulsory
Requirements
This subject has not requirements , but it has got previous capacities
Department
CS
Web
https://www.cs.upc.edu/~turmo/plh/plan15m/PLH.html
The aim of the course is to provide the student with the basics of Natural Language Processing (PLN). Specifically, it introduces the problems presented by PLN, the techniques and resources used to deal with them and the theoretical foundations on which they are based. The course also introduces the most important applications of PLN. The syllabus focuses on the two most used approaches in PLN: the one based on linguistic knowledge and the one based on empirical methods (basically statistical and machine learning).

Teachers

Person in charge

Others

Weekly hours

Theory
1.5
Problems
0.5
Laboratory
2
Guided learning
0
Autonomous learning
6

Competences

Transversal Competences

Transversals

  • CT1 - Entrepreneurship and innovation. Know and understand the organization of a company and the sciences that govern its activity; Have the ability to understand labor standards and the relationships between planning, industrial and commercial strategies, quality and profit.
  • CT2 - Sustainability and Social Commitment. To know and understand the complexity of economic and social phenomena typical of the welfare society; Be able to relate well-being to globalization and sustainability; Achieve skills to use in a balanced and compatible way the technique, the technology, the economy and the sustainability.
  • CT6 [Avaluable] - Autonomous Learning. Detect deficiencies in one's own knowledge and overcome them through critical reflection and the choice of the best action to extend this knowledge.
  • CT8 - Gender perspective. An awareness and understanding of sexual and gender inequalities in society in relation to the field of the degree, and the incorporation of different needs and preferences due to sex and gender when designing solutions and solving problems.

    • Basic

  • CB2 - That the students know how to apply their knowledge to their work or vocation in a professional way and possess the skills that are usually demonstrated through the elaboration and defense of arguments and problem solving within their area of ??study.
  • CB3 - That students have the ability to gather and interpret relevant data (usually within their area of ??study) to make judgments that include a reflection on relevant social, scientific or ethical issues.
  • CB4 - That the students can transmit information, ideas, problems and solutions to a specialized and non-specialized public.
  • CB5 - That the students have developed those learning skills necessary to undertake later studies with a high degree of autonomy

    • Technical Competences

    Especifics

  • CE02 - To master the basic concepts of discrete mathematics, logic, algorithmic and computational complexity, and its application to the automatic processing of information through computer systems . To be able to apply all these for solving problems.
  • CE14 - To master the foundations, paradigms and techniques of intelligent systems and to analyze, designing and build computer systems, services and applications that use these techniques in any field of application, including robotics.
  • CE15 - To acquire, formalize and represent human knowledge in a computable form for solving problems through a computer system in any field of application, particularly those related to aspects of computing, perception and performance in intelligent environments or environments.
  • CE16 - To design and evaluate human-machine interfaces that guarantee the accessibility and usability of computer systems, services and applications.
  • CE17 - To develop and evaluate interactive systems and presentation of complex information and its application to solving human-computer and human-robot interaction design problems.
  • CE18 - To acquire and develop computational learning techniques and to design and implement applications and systems that use them, including those dedicated to the automatic extraction of information and knowledge from large volumes of data.
  • CE27 - To design and apply speech processing techniques, speech recognition and human language comprehension, with application in social artificial intelligence.

    • Generic Technical Competences

    Generic

  • CG3 - To define, evaluate and select hardware and software platforms for the development and execution of computer systems, services and applications in the field of artificial intelligence.
  • CG4 - Reasoning, analyzing reality and designing algorithms and formulations that model it. To identify problems and construct valid algorithmic or mathematical solutions, eventually new, integrating the necessary multidisciplinary knowledge, evaluating different alternatives with a critical spirit, justifying the decisions taken, interpreting and synthesizing the results in the context of the application domain and establishing methodological generalizations based on specific applications.
  • CG5 - Work in multidisciplinary teams and projects related to artificial intelligence and robotics, interacting fluently with engineers and professionals from other disciplines.
  • CG6 - To identify opportunities for innovative applications of artificial intelligence and robotics in constantly evolving technological environments.
  • CG7 - To interpret and apply current legislation, as well as specifications, regulations and standards in the field of artificial intelligence.
  • CG8 - Perform an ethical exercise of the profession in all its facets, applying ethical criteria in the design of systems, algorithms, experiments, use of data, in accordance with the ethical systems recommended by national and international organizations, with special emphasis on security, robustness , privacy, transparency, traceability, prevention of bias (race, gender, religion, territory, etc.) and respect for human rights.
  • CG9 - To face new challenges with a broad vision of the possibilities of a professional career in the field of Artificial Intelligence. Develop the activity applying quality criteria and continuous improvement, and act rigorously in professional development. Adapt to organizational or technological changes. Work in situations of lack of information and / or with time and / or resource restrictions.

    • Objectives

    1. To understand the fundamental theories and techniques associated with Natural Language Processing
      Related competences: CE27, CG3, CG5, CG6, CT6, CB3, CB4, CB5, CE02, CE14, CE18,
    2. To know the most relevant resources and applications of Natural Language Processing
      Related competences: CE27, CG3, CG4, CG5, CG6, CT6, CT8, CB3, CB4, CB5, CE15,
    3. To develop programs to solve particular tasks in the Natural Language Processing area
      Related competences: CE27, CG5, CG7, CG8, CG9, CT1, CT2, CT6, CT8, CB2, CB3, CB4, CB5, CE02, CE14, CE16, CE17, CE18,

    Contents

    1. Natural Language Processing and its applications
    2. Techniques, resources and applications associated with word analysis
    3. Techniques, resources and applications associated with the analysis of word sequences
    4. Techniques, resources and applications associated with sentence analysis
    5. Techniques and applications associated with the analysis of a text seen as a sequence of sentences

    Activities

    Activity Evaluation act


    Sesión introductoria


    Objectives: 1 2
    Contents:
    Theory
    2h
    Problems
    0h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    0h

    Identificación de unidades lingüísticas en un documento


    Objectives: 1 2
    Contents:
    Theory
    2h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Bloc de tratamiento de una palabra


    Objectives: 1 2
    Contents:
    Theory
    8h
    Problems
    3h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Bloc de tratamiento de secuencias de palabras con significado


    Objectives: 1 2
    Contents:
    Theory
    5h
    Problems
    2h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Bloc de tratamiento de una frase aislada



    Theory
    4h
    Problems
    2h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Bloc de tratamiento de un texto como secuencia de frases



    Theory
    1.5h
    Problems
    0.5h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Práctica 1



    Theory
    0h
    Problems
    0h
    Laboratory
    4h
    Guided learning
    0h
    Autonomous learning
    0h

    Pràctica 2



    Theory
    0h
    Problems
    0h
    Laboratory
    8h
    Guided learning
    0h
    Autonomous learning
    0h

    Pràctica 3



    Theory
    0h
    Problems
    0h
    Laboratory
    8h
    Guided learning
    0h
    Autonomous learning
    0h

    Práctica 4



    Theory
    0h
    Problems
    0h
    Laboratory
    8h
    Guided learning
    0h
    Autonomous learning
    0h


    Objectives: 3
    Week: 15 (Outside class hours)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Examen


    Objectives: 1 2
    Week: 15 (Outside class hours)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Teaching methodology

    Evaluation methodology


    NEX: final exam grade
    NLAB: average grade of laboratory practices
    NF: final grade of the course

    NF = 0.5*NEX + 0.5*NLAB

    Reassessment
    Only those students who had previously taken the final exam and failed it can take the reassessment exam. The content of this exam will be all the content of the theory sessions. The maximum grade for this exam will be 7.

    Bibliography

    Basic

    • Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition - Jurafsky, Dan; Martin, James H, Prentice Hall,, 2019.
    • Foundations of Statistical Natural Language Processing - Manning, Christopher; Schütze, Hinrich, MIT Press,, 1999. ISBN: 0262133601
      https://discovery.upc.edu/discovery/fulldisplay?docid=alma991001994779706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
    • The Handbook of Computational Linguistics and Natural Language Processing - Clark, Alexander; Fox, Chris; Lappin, Shalom, Wiley-Blackwell, 2012. ISBN: 9781444324044

    Previous capacities