Identifying Data 2022/23
Subject (*) Natural Language Processing and Text Mining Code 614G02043
Study programme
Grao en Ciencia e Enxeñaría de Datos
Descriptors Cycle Period Year Type Credits
Graduate 2nd four-month period
 Fourth Optional 6
Language
Spanish
Teaching method Face-to-face
Prerequisites
Department Ciencias da Computación e Tecnoloxías da Información
Coordinador
Vilares Calvo, David
 E-mail
david.vilares@udc.es
Lecturers
Gómez Rodríguez, Carlos
Vilares Calvo, David
 E-mail
carlos.gomez@udc.es
david.vilares@udc.es
Web http://campusvirtual.udc.es
General description O procesamento da linguaxe natural (PLN) é a área da intelixencia artificial que se ocupa do estudo e desenvolvemento de modelos computacionais que sexan capaces de procesar e comprender as particularidades da linguaxe natural coa mesma eficacia que os humanos.

Nesta materia, introducirase ao alumnado nos fundamentos básicos e as técnicas de aprendizaxe automática asociadas ao PLN, que se utilizan como punto de partida para o desenvolvemento de numerosas tecnoloxías da linguaxe e a explotación automática de textos.

O alumnado familiarizarase cos algoritmos e técnicas para representar a información latente presente en textos escritos en forma de árbores e grafos, con técnicas para representar palabras de forma que capturen de forma eficiente o seu significado, coa implementación de modelos utilizando técnicas de aprendizaxe automática e a súa aplicación a diversos problemas relacionados co PLN que resulten útiles para usuarios non especializados, así como as técnicas existentes para que as tecnoloxías lingüísticas poidan aplicarse a diversas linguas, incluso aquelas para as que se dispoña de recursos escasos, anotados ou non.

Study programme competencies
Code Study programme competences
A28 CE28 - Comprensión e dominio dos fundamentos e técnicas para o procesado de datos escritos, tanto en linguaxe formal como en linguaxe natural.
B2 CB2 - Que os estudantes saiban aplicar os seus coñecementos ao seu traballo ou vocación dunha forma profesional e posúan as competencias que adoitan demostrarse por medio da elaboración e defensa de argumentos e a resolución de problemas dentro da súa área de estudo
B3 CB3 - Que os estudantes teñan a capacidade de reunir e interpretar datos relevantes (normalmente dentro da súa área de estudo) para emitir xuízos que inclúan unha reflexión sobre temas relevantes de índole social, científica ou ética
B4 CB4 - Que os estudantes poidan transmitir información, ideas, problemas e solucións a un público tanto especializado como non especializado
B7 CG2 - Elaborar adecuadamente e con certa orixinalidade composicións escritas ou argumentos motivados, redactar plans, proxectos de traballo, artigos científicos e formular hipóteses razoables.
B8 CG3 - Ser capaz de manter e estender formulacións teóricas fundadas para permitir a introdución e explotación de tecnoloxías novas e avanzadas no campo.
B9 CG4 - Capacidade para abordar con éxito todas as etapas dun proxecto de datos: exploración previa dos datos, preprocesado, análise, visualización e comunicación de resultados.
B10 CG5 - Ser capaz de traballar en equipo, especialmente de carácter multidisciplinar, e ser hábiles na xestión do tempo, persoas e toma de decisións.

Learning aims
Learning outcomes Study programme competences
To know, understand and analyze natural language processing techniques for processing and disambiguation at syntactic and semantic levels. A28
 B2
B3
B4
B7
B8
B9
B10
To know how to use the techniques and methods of natural language processing to solve real text mining problems. A28
 B2
B3
B4
B7
B8
B9
B10
To know and understand the problems posed by multilingualism in data sources and techniques to solve them. A28
 B2
B8
B9
B10
To know and analyze emerging computing paradigms with the potential to improve parallelism in text mining. A28
 B2
B4
B7
B8

Contents
Topic Sub-topic
Constituent parsing for text mining Syntax of constituents
Statistical constituent analysis with dynamic programming
Analysis of shift-reduce constituents with neural networks
Analysis of discontinuous constituents
Sequence-by-sequence constituent analysis
Dependency parsing for text mining
 Dependency Syntax
Annotation criteria and universal dependencies
Dependency analysis based on transitions
Analysis of dependencies based on graphs
Non-projectivity
Semantics Analysis of semantic dependencies
Dense vectors using SVD
Dense vectors using word prediction: skip-gram and CBOW
Properties of dense vectors
Brown clustering
Computing with word senses Word senses
Relations between senses
Databases of lexical relationships
Disambiguation of the meaning of words
Practical applications of text mining -
Multilingual language processing Processing of morphologically-rich languages
Non-segmented language processing
Language processing with few resources
Translingual processing
Emerging technologies -

Planning
Methodologies / tests Competencies Ordinary class hours Student’s personal work hours Total hours
Guest lecture / keynote speech A28 B3 B8 B9 21 21 42
Laboratory practice A28 B2 B4 B7 B9 B10 10 30 40
Problem solving A28 B2 B3 B8 8 30 38
Objective test A28 B2 B3 B8 2 10 12
Supervised projects A28 B3 B4 B7 B10 1 16 17
 
Personalized attention 1 0 1
 
(*)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 in advance and the teacher will promote an active attitude, asking questions that allow clarifying specific aspects and leaving questions open for the student's reflection.
Laboratory practice Practical classes with the use of a computer, which allow the student to familiarize himself/herself from a practical point of view with the issues exposed in the theoretical classes.
Problem solving Problem-based learning, seminars, case studies and projects.
Objective test Mastery of theoretical and operational knowledge of the subject will be assessed.
Supervised projects Work in which students will consult sources of information to become familiar with research aspects of the field

Personalized attention
Methodologies
Objective test
Guest lecture / keynote speech
Laboratory practice
Supervised projects
Problem solving
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. In supervised projects, personalized attention will be provided to students to guide them in their autonomous work.

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
Objective test A28 B2 B3 B8 Compulsory performance. Mastery of theoretical and operational knowledge of the subject will be assessed. 50
Laboratory practice A28 B2 B4 B7 B9 B10 The deliveries of the practices must be made within the period established in the virtual campus and must follow the specifications indicated in the statement both for their submission and their defense. 40
Supervised projects A28 B3 B4 B7 B10 The students abilities to understand and assimilate research work will be evaluated. 10
 
Assessment comments
Students must achieve at least 40% of the maximum mark of the theory and practice parts, and in any case the sum of three parts must exceed 5 to pass the subject. If any of the above requirements is not met, the grade of the call will be established according to the lowest grade obtained.

In case of not reaching the minimum in theory or practice, the student will have a second opportunity in which they will only be required to deliver said part. The tutored works are considered as continuous evaluation and will not be delivered in the second opportunity.

Grades will not be saved between academic courses.

The deliveries of the practices must be made within the period established in the virtual campus and must follow the specifications indicated in the statement both for their submission and their defense.

Whoever attends the objective test in the official evaluation period will have the status of "Presented".

In the case of fraudulent completion of exercises or tests, the Regulations for evaluating the academic performance of students and reviewing qualifications will be applied. In application of the corresponding regulations on plagiarism, the total or partial copy of some practice or theory exercise will suppose the suspense in the two opportunities of the course, with the qualification of 0.0 in both cases.

Sources of information
Basic 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


Recommendations
Subjects that it is recommended to have taken before
Written Language Processing/614G02029
Machine Learning III/614G02026
Machine Learning I/614G02019
Machine Learning II/614G02021

Subjects that are recommended to be taken simultaneously

Subjects that continue the syllabus

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.