- Knowledge of text preprocessing and transformation techniques. - Knowledge of the main Deep Learning architectures for learning vector representations of text. - Knowledge of the key NLP tasks and application contexts (entity recognition, question answering, intent detection, text categorization, machine translation, sentiment analysis). - Ability to design and implement a recommender system. - Knowledge of the fundamentals of Generative AI applied to text and ability to use Large Language Models. - Ability to study, design, implement, and test a text summarization algorithm. - Ability to use NoSQL databases to store and query textual data. - Ability to design a full NLP pipeline, including the requirement analysis, methodology design and implementation, performance assessment, and result visualization.

- Fundamentals of data sciences, machine learning, and deep learning. - Basic knowledge of the Python language.

The course covers the following topics: - Natural Language Processing fundamentals: text characteristics, text preparation, topic modelling, overview of the main NLP applications (1.25 cr.) - Vector representations of text: word embedding architectures and shallow sentence embedding architectures (1.25 cr.) - Contextualized embedding e attention mechanism (0.9 cr.) - Entity Recognition, Intent Detection e Question Answering (1.2 cr.) - Text summarization (0.6 cr.) - Natural Language Generation and Large Language Models (0.6 cr.) - Machine Translation (0.4 cr.) - Recommender Systems (0.4 cr.) - Application of NO SQL Databases for Information Retrieval (0.4 cr.) - Text Categorization and Sentiment Analysis (0.4 cr.) - NLP pipeline design: requirement analysis, methodology design and implementation, empirical assessment, outcome presentation (0.6 cr.)

The course includes lectures in the classroom (4.1 cr.), whose topics are described earlier, and practices on the lecture topics (1.8 cr.), and in particular text preprocessing, word and sentence embeddings, Transformers, information retrieval, machine translation, Large Language Models, text summarization, text categorization, recommender systems, and NLP pipeline design. Students will develop a team project and deliver a written report. The report will contribute to the final exam grade. The course includes laboratory sessions (2.1 cr.) on text preparation, word and sentence embeddings, Transformers, intent detection, recommender systems, text summarization, machine translation, Large Language Models, and chatbot architectures. Laboratory sessions allow experimental activities on the most widespread open-source products.

Class handouts will be made available through the didactic portal. Additional readings (covering the most relevant course topics): - Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning. Mohammad Taher Pilehvar, Jose Camacho-Collados. Morgan & Claypool. ISBN: 9781636390215 - Neural Network Methods for Natural Language Processing. Yoav Goldberg. Morgan & Claypool. ISBN: 9781627052955 - Deep Learning in Natural Language Processing. Li Deng and Yang Liu Editors. Springer. ISBN: 9789811052088 - Natural Language Processing with Transformers. Lewis Tunstall, Leandro von Werra, and Thomas Wolf. Released February 2022. Publisher(s): O'Reilly Media, Inc. ISBN: 9781098103248

Lecture slides; Text book; Exercises; Exercise with solutions ; Lab exercises; Lab exercises with solutions; Video lectures (previous years);

Exam: Written test; Group project;

The exam comprises two main mandatory parts: 1) a written test on the theoretical aspects introduced during the course (closed and/or open-ended questions) (max. 22 points). 2) the discussion and evaluation of the final report on a team project assigned during the course (max. 10 points). The final score is given by the sum of the points achieved in the written part and in the evaluation of the final report. 30 with honors (30L) will be assigned if the final score is equal to or above 31. Learning objectives assessment: The written part will assess - NLP fundamentals - Embedding models - Recommender Systems - Text categorization and sentiment analysis - Natural Language Generation and Large Language Models - Information Retrieval - Other NLP applications covered by the course (e.g., Entity Recognition, Intent Detection, Question Answering, Text Summarization, Machine Translation) The team project will assess - the ability to define the requirements and the problem statement. - the ability to study, design, and implement a full NLP pipeline. - the ability to develop and test efficient and effective text analytics solutions, - the ability to setup, run, and collect a sufficiently large set of empirical results, - the ability to present the requirement analysis, the methodology design, and the outcomes of the empirical analyses. In the written part the points assigned to each question/exercise will be clearly indicated next to the question/exercise. Wrong answers may cause a score penalty. Missing answers will receive no penalty. The written exam lasts 1 hour and is closed-book. Electronic devices are not allowed.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.