Mining Unstructured Data

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Credits
6
Types
Compulsory
Requirements
This subject has not requirements, but it has got previous capacities
Department
CS
The goal of this course is to provide the fundamentals of Natural Language Processing (NLP) to the student. Concretely, the course is an introduction to the most relevant drawbacks involved in NLP, the most relevant techniques and resources used to tackle with them, and the theories they are based on. In addition, brief descriptions of the most relevant NLP applications are included.
The flow of the course is along two main axis: (1) computational formalisms to describe natural language processes, and (2) statistical and machine learning methods to acquire linguistic models from large data collections and solve specific linguistic tasks

Teachers

Person in charge

  • Lluis Padro Cirera ( )

Weekly hours

Theory
1.5
Problems
0.5
Laboratory
1
Guided learning
0
Autonomous learning
0

Competences

Transversal Competences

Information literacy

  • CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.

Third language

  • CT5 - Achieving a level of spoken and written proficiency in a foreign language, preferably English, that meets the needs of the profession and the labour market.

Entrepreneurship and innovation

  • CT1 - 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. Being aware of and understanding the mechanisms on which scientific research is based, as well as the mechanisms and instruments for transferring results among socio-economic agents involved in research, development and innovation processes.

Basic

  • CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.
  • CB7 - Ability to integrate knowledge and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.
  • CB8 - Capability to communicate their conclusions, and the knowledge and rationale underpinning these, to both skilled and unskilled public in a clear and unambiguous way.
  • CB9 - Possession of the learning skills that enable the students to continue studying in a way that will be mainly self-directed or autonomous.
  • CB10 - Possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context.

Generic Technical Competences

Generic

  • CG2 - Identify and apply methods of data analysis, knowledge extraction and visualization for data collected in disparate formats

Technical Competences

Especifics

  • CE6 - Design the Data Science process and apply scientific methodologies to obtain conclusions about populations and make decisions accordingly, from both structured and unstructured data and potentially stored in heterogeneous formats.
  • CE7 - Identify the limitations imposed by data quality in a data science problem and apply techniques to smooth their impact
  • CE11 - Analyze and extract knowledge from unstructured information using natural language processing techniques, text and image mining
  • CE12 - Apply data science in multidisciplinary projects to solve problems in new or poorly explored domains from a data science perspective that are economically viable, socially acceptable, and in accordance with current legislation
  • CE13 - Identify the main threats related to ethics and data privacy in a data science project (both in terms of data management and analysis) and develop and implement appropriate measures to mitigate these threats

Objectives

  1. Know and understand basic NLP tasks and their application to text analysis.
    Related competences: CB10, CB6, CB7, CT4, CT5, CE11, CE6, CE7, CG2,
  2. Know, understant, and apply text mining techniques, including entity recognition, sentiment analysis, and document retrieval.
    Related competences: CT4, CT5, CE11, CE12, CB6, CB8, CB9, CT1,
  3. Know, understand, and apply basic principles of deep learning in unstructured data tasks, such as natural language processing, or computer vision.
    Related competences: CE13, CB6, CB7, CB8, CB10, CB9, CT4, CT5, CE11, CE7, CG2, CE6,

Contents

  1. Natural language processing and its application to text analysis
    Introduction: What is NLP and its applications
  2. natural language processing stages
    Text segmentation: sentence splitting, tokenization; morpholigcal analysis, PoS tagging, syntactic parsing
  3. text classification, text similarity.
    Similarity measures for text. String edit based distances. Vector and set distance measures, distributional semantics. Document retrieval.
    Text classification: Sentiment analysis
  4. Information extraction: Entity recognition, relation extraction
  5. Deep learning techniques for the analysis of non-structured data
    Word embeddings, neural language processing
  6. Main deep learning architectures for non-structured data
    Recurrent NN, Convolutional NN, Transformers

Activities

Activity Evaluation act


lab project


Objectives: 3
Week: 16
Type: assigment
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
40h

Final exam


Objectives: 1 2
Week: 16
Type: final exam
Theory
3h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
40h

NLP and its applications

Introduction. What is NLP, tasks, components, and applications.
Objectives: 1
Contents:
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

natural language processing stages

Text segmentation: sentence splitting/tokenization; morphological analysis; PoS tagging; syntactic parsing.
Objectives: 1
Contents:
Theory
6h
Problems
2h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Text classification, text similarity

Similarity measures for text. String edit based distances. Vector and set distance measures, distributional semantics. Document retrieval. Text classification: Sentiment analysis
Objectives: 2
Contents:
Theory
2h
Problems
0.5h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Information extraction: Entity recognition, relation extraction


Objectives: 1 2
Contents:
Theory
2h
Problems
0.5h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Deep learning techniques for the analysis of non-structured data

Word embeddings, neural language processing
Objectives: 3
Contents:
Theory
6h
Problems
1h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Main deep learning architectures for non-structured data

Recurrent NN, Convolutional NN, Transformers
Objectives: 3
Contents:
Theory
6h
Problems
1h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Theory
0h
Problems
0h
Laboratory
5h
Guided learning
0h
Autonomous learning
6h

Theory
0h
Problems
0h
Laboratory
5h
Guided learning
0h
Autonomous learning
6h

Theory
0h
Problems
0h
Laboratory
5h
Guided learning
0h
Autonomous learning
6h

Teaching methodology

Participative lectures with theoretical and practical content
Practical sessions with student participation for the resolution of exercises related to the course contents
lab project - team work
Consulting sessions

Evaluation methodology

Lab project 50% + final exam 50%

Bibliography

Basic:

Previous capacities

Advanced skills on python programming
Math and statistics skills to the level of an engineering/tech/science university degree
Fundamentals of machine learning