Study programme competencies |
Code
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Study programme competences / results
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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 / results |
To know, understand and analyze the formal representation of diverse lexical, syntactic and semantic phenomena of natural language. |
AC1
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BC1 BC3 BC4 BC6 BC10
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CC2 CC8
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To know, understand and know how to use the technologies, frameworks and libraries for the construction of natural language processing systems. |
AC1 AC2
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BC3 BC4 BC6 BC7 BC10
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CC2 CC3 CC7
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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
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BC1 BC3 BC4 BC6 BC7 BC10
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CC2 CC3 CC7 CC8
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To know, understand and analyze natural language processing techniques for processing and disambiguation at the lexical, syntactic and semantic levels. |
AC1 AC2 AC3
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BC1 BC3 BC4 BC6 BC7 BC10
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CC2 CC3 CC7 CC8
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To know and understand the problems posed by ambiguity and imprecision in natural language data sources and techniques to solve them. |
AC1 AC2
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BC1 BC3 BC4 BC6 BC7 BC10
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CC2 CC3 CC7 CC8
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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 / Results |
Teaching hours (in-person & virtual) |
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. |
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Assessment |
Methodologies
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Competencies / Results |
Description
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Qualification
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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. |
50 |
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. |
50 |
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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.
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.
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Sources of information |
Basic
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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/ |
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Complementary
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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 |
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Subjects that are recommended to be taken simultaneously |
Machine Learning I /614544012 |
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Subjects that continue the syllabus |
Text Mining/614544011 | Language Modelling/614544009 | Web Intelligence and Semantic Technologies/614544010 |
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Other comments |
As established in the relevant regulations, this subject incorporates gender perspective. Participation of male and female students will be encouraged. We will work to identify sexist prejudices and actitudes and will influence the surroundings to modify them and promote values of respect and equality. Gender discrimination will be detected, and actions and measures proposed to correct it. |
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