Identifying Data 2022/23
Subject (*) Natural Language Understanding Code 614544008
Study programme
Máster Universitario en Intelixencia Artificial
Descriptors Cycle Period Year Type Credits
Official Master's Degree 1st four-month period
 First Obligatory 6
Language
English
Teaching method Face-to-face
Prerequisites
Department Ciencias da Computación e Tecnoloxías da Información
Coordinador
Gómez Rodríguez, Carlos
 E-mail
carlos.gomez@udc.es
Lecturers
Gómez Rodríguez, Carlos
Vilares Calvo, David
Vilares Ferro, Jesus
 E-mail
carlos.gomez@udc.es
david.vilares@udc.es
jesus.vilares@udc.es
Web http://campusvirtual.udc.es
General description A materia introduce os conceptos e técnicas básicas asociadas ao procesamento da linguaxe natural, punto de partida para o deseño de contornas de explotación de información e de diálogo baseadas na linguaxe humana, tanto a nivel léxico como sintáctico, semántico e pragmático.

O obxectivo é introducir ao estudante na complexidade inherente á análise da linguaxe natural humana, fundamentalmente asociada á ambigüidade e dependencias contextuais que presenta, e no deseño de estruturas de datos e algoritmos que permitan o seu tratamento práctico.

Study programme competencies
Code Study programme competences
A2 CE01 - Understanding and command of techniques for lexical, syntactic and semantic processing of text in natural language
A3 CE02 - Understanding and command of fundamentals and techniques for processing linked documents, both structured and unstructured, and of the representation of their contents
A4 CE03 - Understanding and knowledge of the techniques for knowledge representation and processing for ontologies, graphs and RDF, together with their associated tools
B1 CG01 - Maintaining and extending theoretical foundations to allow the introduction and exploitation of new and advanced technologies in the field of AI
B3 CG03 - Searching and selecting that useful information required to solve complex problems, with a confident handling of bibliographical sources in the field
B4 CG04 - Suitably elaborating written essays or motivated arguments, including some point of originality, writing plans, work projects, scientific papers and formulating reasonable hypotheses in the field
B6 CB01 - Acquiring and understanding knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, frequently in a research context
B7 CB02 - The students will be able to apply the acquired knowledge and to use their capacity of solving problems in new or poorly explored environments inside wider (or multidisciplinary) contexts related to their field of study
B10 CB05 - The students will acquire learning abilities to allow them to continue studying in way that will mostly be self-directed or autonomous
C2 CT02 - Command in understanding and expression, both in oral and written forms, of a foreign language
C3 CT03 - Use of the basic tools of Information and Communications Technology (ICT) required for the student's professional practice and learning along her life
C7 CT07 - Developing the ability to work in interdisciplinary or cross-disciplinary teams to provide proposal that contribute to a sustainable environmental, economic, political and social development
C8 CT08 - Appreciating the importance of research, innovation and technological development in the socioeconomic and cultural progress of society

Learning aims
Learning outcomes Study programme competences
To know, understand and analyze the formal representation of diverse lexical, syntactic and semantic phenomena of natural language. AC1
 BC1
BC3
BC4
BC6
BC10
 CC2
CC8
To know, understand and know how to use the technologies, frameworks and libraries for the construction of natural language processing systems. AC1
AC2
 BC3
BC4
BC6
BC7
BC10
 CC2
CC3
CC7
To design, implement and know how to use algorithms and data structures to treat and support the various phenomena characteristic of natural language. AC1
AC2
AC3
 BC1
BC3
BC4
BC6
BC7
BC10
 CC2
CC3
CC7
CC8
To know, understand and analyze natural language processing techniques for processing and disambiguation at the lexical, syntactic and semantic levels. AC1
AC2
AC3
 BC1
BC3
BC4
BC6
BC7
BC10
 CC2
CC3
CC7
CC8
To know and understand the problems posed by ambiguity and imprecision in natural language data sources and techniques to solve them. AC1
AC2
 BC1
BC3
BC4
BC6
BC7
BC10
 CC2
CC3
CC7
CC8

Contents
Topic Sub-topic
Introduction. Levels of analysis.
Ambiguity and contextual dependencies.
Lexical analysis. Segmentation.
Dictionaries and thesauri.
Part-of-speech tagging.
Syntactic parsing. Algebraic grammars.
Mildly context-sensitive grammars.
Dependency grammars.
Probabilistic grammars.
Semantic parsing. Lexical semantics.
Semantic dependencies.
Semantic graphs.

Planning
Methodologies / tests Competencies Ordinary class hours Student’s personal work hours Total hours
Guest lecture / keynote speech A2 A3 A4 B1 B3 B6 B7 B10 C2 C8 21 21 42
Laboratory practice A2 A3 A4 B3 B4 B6 B7 B10 C2 C3 C7 C8 14 48 62
Problem solving A2 A3 A4 B3 B4 B6 B7 B10 C2 7 25 32
Objective test A2 A3 A4 B1 B6 B7 C2 3 9 12
 
Personalized attention 2 0 2
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies Description
Guest lecture / keynote speech Theoretical classes, in which the content of each topic is exposed. The student will have copies of the slides beforehand and the professor will promote an active attitude, asking questions to clarify specific aspects and leaving open questions for the student's reflection.
Laboratory practice Practical classes with the use of computers, which allow the student to familiarize himself/herself from a practical point of view with the issues presented in the theoretical classes.
Problem solving Problem-based learning, seminars, case studies and projects.
Objective test The mastery of the theoretical and operating knowledge of the subject will be evaluated.

Personalized attention
Methodologies
Guest lecture / keynote speech
Laboratory practice
Problem solving
Objective test
Description
The development of the master classes, as well as of the problem solving classes and the practical laboratories, will be carried out according to the progress of the students in the comprehension and assimilation of the contents taught. The general progress of the class will be combined with a specific attention to those students who present greater difficulties in the task of learning and with an additional support to those who present greater fluency and wish to broaden their knowledge.

With regard to individual tutorials, given their personalized nature, they should not be devoted to extend the contents with new concepts, but to clarify the concepts already exposed. The teacher will use them as an interaction that will allow them to draw conclusions regarding the degree of assimilation of the subject by the students.

Assessment
Methodologies Competencies Description Qualification
Laboratory practice A2 A3 A4 B3 B4 B6 B7 B10 C2 C3 C7 C8 The delivery of the practicals must be done within the deadline established in the virtual campus and must follow the specifications indicated in the statement for both presentation and defense. 40
Objective test A2 A3 A4 B1 B6 B7 C2 Compulsory realization. The mastery of the theoretical and operative knowledge of the subject will be evaluated. 60
 
Assessment comments
Students must achieve at least 40% of the maximum grade for each part (theory, practice) and in any case the sum of both parts must reach a 5 to pass the course. If any of the above requirements is not met, the grade for the course will be established according to the lowest grade obtained. 

In case of not reaching the minimum grade in one of the parts, the student will have a second opportunity in which only the delivery of that part will be required. 

Grades will not be kept between academic years. 

The delivery of the practicals must be done within the deadline established in the virtual campus and must follow the specifications indicated in the statement for both its presentation and defense.

The student who submits all the compulsory practicals or attends the objective test in the official evaluation period will be considered "Presented".

In the case of fraudulent performance of exercises or tests, the Regulations for the evaluation of students' academic performance and review of qualifications will be applied. In application of the corresponding regulations on plagiarism, the total or partial copy of any practical or theory exercise will result in failure in both opportunities of the course, with a grade of 0.0 in both cases.

Sources of information
Basic Manning, C., & Schutze, H. (1999). Foundations of statistical natural language processing. MIT Press
Manning, C., & Schutze, H. (1999). Foundations of statistical natural language processing. MIT Press
Jacob Eisenstein (2019). Introduction to Natural Language Processing. MIT Press
Goldberg, Y. (2017). Neural network methods for natural language processing. Synthesis lectures on human language technologies. Morgan Claypool
Jurafsky, D. & Martin, J. H. (2022). Speech and Language Processing (3rd ed. draft). Disponible en: https://web.stanford.edu/~jurafsky/slp3/

Complementary Stuart Russell, Peter Norvig (2020). Artificial Intelligence: A Modern Approach, 4th Edition. Pearson
Kübler, S., McDonald, R., & Nivre, J. (2009). Dependency Parsing. Synthesis lectures on human language technologies. Morgan Claypool
Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze (2008). Introduction to Information Retrieval. Cambridge University Press, Cambridge
Chollet, F. (2018). Keras: The python deep learning library. Astrophysics Source Code Library

Adicionalmente, manexaranse textos científicos dispoñibles nas bibliotecas dixitais da área, como o ACL Anthology ou ACM.


Recommendations
Subjects that it is recommended to have taken before

Subjects that are recommended to be taken simultaneously
Machine Learning I  /614544012

Subjects that continue the syllabus
Text Mining/614544011
Language Modelling/614544009
Web Intelligence and Semantic Technologies/614544010

Other comments


(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.