Text Analytics

Graduate Programs in Data Science & Business Informatics and in Digital Humanities
WDB-LM, WFU-LM

Academic Year 2023-2024, first semester

• WHERE: Polo Didattico "L. Fibonacci", Via F. Buonarroti 4, Pisa
• WHEN:
  • Thursday, 16:00-18:00 - Fib C1 (Polo Fibonacci B)
  • Friday, 11:00-13:00 - Fib M1 (Polo Fibonacci B)
• OFFICE HOURS: Thursday, 14:00-16:00 (by appointment) - room 288 @ Dpt. Computer Science
• WHAT: Programme 2023-2024 - 635AA.

Syllabus

The course wil cover the following topics:

• Background: Natural Language Processing, Information Retrieval and Machine Learning
• Mathematical background: Probability, Statistics and Algebra
• Linguistic essentials: words, lemmas, morphology, Part of Speech (PoS), syntax
• Basic text processing: regular expression, tokenisation
• Data collection: scraping
• Basic modelling: collocations, language models
• Introduction to Machine Learning: theory and practical tips
• Libraries and tools: NLTK, Spacy, Keras, pytorch
• Classification/Clustering
• Sentiment Analysis/Opinion Mining
• Information Extraction/Relation Extraction/Entity Linking
• Transfer learning
• Quantification

Slides & Materials [DidaWiki]

All the slides and the materials are available on the Text Analytics page of DidaWiki.

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- Lesson 1 - 21/09/23: Introduction to the course, NLP & Text Analytics.

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Learning Outcomes

Knowledge. Learning essential techniques, algorithms, and models used in natural language processing. Understanding of the architectures of typical text analytics applications and of libraries for building them. Expertise in design, implementation, and evaluation of applications that exploit analysis, interpretation, and transformation of texts.

Assessment criteria of knowledge. The student will be assessed on the demonstrated ability to discuss the course contents using the appropriate terminology and to apply natural language processing techniques.

Skills. The student will be able to design, implement and evaluate applications that exploit the analysis, interpretation, and transformation of texts.

Assessment criteria of skills. Attending students will be asked to participate in a group project aimed at assessing skills in the design and implementation of a text analytics task agreed upon with the teacher. Non-attending students will be asked to solve exercises during a written exam and oral discussion.

Behaviors. Students will be able to analyze a text processing problem, select the correct methods to solve it, and implement a working solution. They will be aware of several issues related to the processing of text, including the reliability of the results, when applications involve human-annotated (subjective) - data.

Assessment criteria of behaviors. The behavior of students will be assessed during project development and/or at the written/oral exam.