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<!DOCTYPE HTML>
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<title>LP | TXA 25/26</title>
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<h1>Text Analytics 2025/2026</h1>
<p>TXA (635AA), 6 CFU<br />
Graduate Programs in Data Science & Business Informatics and in Digital Humanities WDB-LM, WFU-LM</p>
</header>
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<div class="row">
<div class="col-6 col-12-small">
<h2>General Information</h2>
<p>Academic Year 2025-2026, first semester</p>
<ul class="alt">
<li><strong>WHERE: </strong> Polo Didattico "L. Fibonacci", Via F. Buonarroti 4, Pisa</li>
<li><strong>WHEN: </strong>
<ul class="no-alt">
<li>Thursday, 14:00-16:00 - Fib M1 (Polo Fibonacci B)</li>
<li>Friday, 11:00-13:00 - Fib L1 (Polo Fibonacci B)</li>
</ul></li>
<li><strong>OFFICE HOURS: </strong> Tuesday 14:00-16:00 - room 288 @ Dpt. Computer Science (by appointment)</li>
<li><strong>WHAT: </strong> <a href="https://unipi.coursecatalogue.cineca.it/insegnamenti/2025/52766_691597_74512/2022/52766/11357?coorte=2024&schemaid=9090" target="_blank"> Catalogue: TXA Programme 2025-2026 - 635AA</a>.</li>
</ul>
</div>
<div class="col-6 col-12-small">
<h2>Objectives</h2>
<p>The course targets text analytics systems and applications to respond to business problems by discovering and presenting
knowledge that is otherwise locked in textual form. The main objectives of the course are:</p>
<ul>
<li>Learning essential techniques, algorithms, and models used in natural language processing.</li>
<li>Understanding of the architectures of typical text analytics applications and of libraries for building them.</li>
<li>Expertise in design, implementation, and evaluation of applications that exploit analysis, interpretation, and transformation of texts.</li>
</ul>
</div>
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<hr />
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<h2 id="content">Exam modalities</h2>
<p>Students will be assessed through a written exam and an oral exam. Each student will be evaluated based on their ability to discuss the course content using appropriate terminology and to practically apply natural language processing techniques.</p>
<div class="row">
<div class="col-6 col-12-small">
<h3>Attending Students</h3>
<p>The exam consists of a written test and an oral test.
The exam consists of a written test and an oral test. For the written part, attending students can choose between a traditional
written exam, with questions on the topics covered in the course, or a group project carried out during the course period,
designed to assess the ability to plan and implement a text analysis task agreed upon with the instructor. In both cases,
the oral test follows: students who have completed the project are required to present and discuss it,
including individual questions to verify each member’s understanding, and all students, regardless of the chosen option,
are further examined on the remaining topics of the course.</p>
</div>
<div class="col-6 col-12-small">
<h3>Non-Attending Students</h3>
<p>The exam consists of a written test and an oral test. Non-attending students will be required to answer questions and solve exercises during a written exam.
<br />No materials other than the slides, notebook experimentation, and the selected chapters from the books are required.
</p>
</div>
</div>
<hr />
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<h2>Slides & Materials</h2>
<p>Students are required to study the slides, actively work through and experiment with the notebooks, and read the <strong> selected chapters </strong> from:</p>
<ul>
<li>D. Jurafsky, J.H. Martin, <a href="https://web.stanford.edu/~jurafsky/slp3/" target="_blank"> Speech and Language Processing</a>. 3nd edition, Prentice-Hall, 2018.</li>
<li>S. Bird, E. Klein, E. Loper. <a href="https://www.nltk.org/book/" target="_blank"> Natural Language Processing with Python.</a></li>
<li>J. Eisenstein. <a href="https://cseweb.ucsd.edu/~nnakashole/teaching/eisenstein-nov18.pdf" target="_blank"> Introduction to Natural Language Processing. MIT Press, 2019.</a></li>
<li>Zhai and Massung. <a href="https://dl.acm.org/doi/book/10.1145/2915031" target="_blank">Text Data Management and Analysis. </a>Morgan & Claypool Publishers, 2016. </li>
</ul>
<div class="table-wrapper">
<table class="alt">
<thead>
<tr>
<th>Date</th>
<th>Lecture</th>
<th>Slides</th>
<th>Material / Reference</th>
</tr>
</thead>
<tbody>
<tr>
<td>25/09/2025</td>
<td>Introduction to the course, NLP & Text Analytics.</td>
<td><a href="https://drive.google.com/file/d/138lq3oONM8SlOD7Z3__atzB9sq6GaaTT/view?usp=sharing" target="_blank"> 1 - Introduction to the Text Analytics course</a></td>
<td>
J. Eisenstein. Introduction to Natural Language Processing. MIT Press. <a target="_blank" href="https://drive.google.com/file/d/1y6rFLb7ZtvYs3h8Zv5mXbP7ohHtRwBtL/view?usp=drive_link"> Chp. 1.</a>
</td>
</tr>
<tr>
<td>26/09/2024</td>
<td>
<ul>
<li>Introduction to Python</li>
<li>Text Indexing</li>
<li>Regex</li>
</ul>
</td>
<td><a href="https://drive.google.com/file/d/1enLlaxkQyo1nRvg9UE3rG-w63XQbFNK6/view?usp=drive_link" target="_blank"> 2- Introduction to Python & Text Indexing</a></td>
<td>
<ul>
<li>Notebook <a target="_blank" href="https://drive.google.com/file/d/1rZBv0hYTroWB60lm5U4ShXKGS92bAoHe/view?usp=drive_link"> Python basics </a></li>
<li>Notebook <a target="_blank" href="https://drive.google.com/file/d/1NN1sGbKcggzl5H4BXE9ElQc7h0HU3mIJ/view?usp=drive_link"> Strings basics </a></li>
<li>Notebook <a target="_blank" href="https://drive.google.com/file/d/19ciJhxCDN0nkGjfx8o1cQ0fy73dWZ2tf/view?usp=drive_link"> Regular expressions </a></li>
<li>D. Jurafsky, J.H. Martin, <a href="https://drive.google.com/file/d/1PH6MliJ1mpcw3smWagzwuzF44DkpXzVZ/view?usp=drive_link" target="_blank">Ch. 2</a>.</li>
</ul></td>
</tr>
<tr>
<td>2/10/2025</td>
<td>Reminds on Probability</td>
<td><a href="https://drive.google.com/file/d/1Sp6bAlEWta7GxrteqqTNg0myXubJWntv/view?usp=drive_link" target="_blank"> 3 - Reminds on Probability</a></td>
<td></td>
</tr>
<tr>
<td>9/10/2025</td>
<td>Probabilistic Language Models</td>
<td><a href="https://drive.google.com/file/d/1MyQGeIVR-9bnoh1pRRsQs0riANNHBbMJ/view?usp=drive_link" target="_blank"> 4 - Probabilistic Language Models</a></td>
<td>Notebook <a target="_blank" href="https://drive.google.com/file/d/1Nu5y583N3gccoVBJFSDN-BTvwniXowg5/view?usp=drive_link"> Probabilistic Language Models </a></td>
</tr>
<tr>
<td>10/10/2025</td>
<td>Probabilistic Language Models</td>
<td><i>5 - Practice on Probabilistic Language Models</i></td>
<td>
<ul>
<li>D. Jurafsky, J.H. Martin, <a href="https://drive.google.com/file/d/1GXFESuUvSzMfYAMCv7XvwbDYsIrJKWdk/view?usp=drive_link" target="_blank">Ch. 3</a>.
</li>
<li>Notebook <a target="_blank" href="https://drive.google.com/file/d/1Btpl3hamGGk_agmviCrs6Hrl-opTT1ZW/view?usp=drive_link"> Probabilistic Language Models (part 2)</a>
</li>
</ul>
</td>
</tr>
<tr>
<td>16/10/2025</td>
<td>Text Indexing</td>
<td>
<a href="https://drive.google.com/file/d/18EqWt0fLOQxXTqRy75WZ1TCRtqYmm3v9/view?usp=drive_link" target="_blank"> 6 - Text Indexing (part. 2)</a>
</td>
<td>
<ul>
Notebook <a target="_blank" href="https://drive.google.com/file/d/1F2cgT21GuyifhYuG2vTymUgh-OYpubmk/view?usp=drive_link">Collocations (NLTK, Gensim)</a>
</ul>
</td>
</tr>
<tr>
<td>17/10/2025</td>
<td>Text Indexing</td>
<td>
<a href="https://drive.google.com/file/d/1SJPN1-DYNItt9wJbygSwCMX3fP1RcSd_/view?usp=drive_link" target="_blank"> 7 - Text Indexing (part. 3)</a>
</td>
<td>
D. Jurafsky, J.H. Martin. Chps. <a target="_blank" href="https://drive.google.com/file/d/1onfMXpkOcoF7mKPtgHy8WWbXnDONFGBr/view?usp=drive_link">17</a> (excluding chs. 17.4 and 17.5), <a target="_blank" href="https://drive.google.com/file/d/1sjKHVynoY9dKgBjmKHlKdekcXr3EDDP2/view?usp=drive_link">19</a> (Introduction and ch. 19.1 only), <a target="_blank" href="https://drive.google.com/file/d/1ogGgpUa-7yu9JnRFZkJje_8Vyf6TXMdE/view?usp=drive_link">22</a> (excluding chapters 22.4 and 22.5.19)
</td>
</tr>
<tr>
<td>23/10/2025</td>
<td>Text Indexing</td>
<td>
<a href="https://drive.google.com/file/d/1D8XdC52BPaakyfYgoZUb93U21NCVcDU8/view?usp=drive_link" target="_blank"> 8 - Text Indexing (part. 4)</a>
</td>
<td>
</td>
</tr>
<tr>
<td>24/10/2025</td>
<td>Lesson Cancelled</td>
<td></td>
<td></td>
</tr>
<tr>
<td>30/10/2025</td>
<td>Text Indexing</td>
<td>
<a href="https://drive.google.com/file/d/1iE6DfLI_RWSOm1e6cKm8wIjN73ReLLIa/view?usp=drive_link" target="_blank"> 9 - Text Indexing (part. 5)</a>
</td>
<td>
<ul>
<li>
Notebook <a target="_blank" href="https://drive.google.com/file/d/1fgFhETtCg-V54GRpVtMNw1apL_Dn6oim/view?usp=drive_link">Text Processing to Vectorization</a>
</li>
<li>
D. Jurafsky, J.H. Martin. Chp. <a target="_blank" href="https://web.stanford.edu/~jurafsky/slp3/J.pdf">J (PMI)</a>
</li>
</ul>
</td>
</tr>
<tr>
<td>31/10/2025</td>
<td>Vector Space Models</td>
<td>
<a href="https://drive.google.com/file/d/1yCaG4bNc-Sovh-Jd3RpCOxPnvgSCEqTm/view?usp=drive_link" target="_blank"> 10 - VSM (Correct Version)</a>
</td>
<td>
<ul>
<li>
Notebook <a target="_blank" href="https://drive.google.com/file/d/17KuRHsiriKlGtEtIUVmxjXvc4uokfAx_/view?usp=drive_link">VSM</a>
</li>
<li>
D. Jurafsky, J.H. Martin. Chp. <a target="_blank" href="https://drive.google.com/file/d/1D2JxyN5VZXiQD_X4yVePfIJjmGVzABsM/view?usp=drive_link">6</a>
</li>
</ul>
</td>
</tr>
<tr>
<td>6/11/2025</td>
<td>
Machine Learning for Text Analytics
</td>
<td>
<a href="https://drive.google.com/file/d/1cAyaaOOiNNj7L_xX5bsjwju1Bup1JqiJ/view?usp=drive_link" target="_blank"> 11 - Machine Learning for Text Analytics (part. 1)</a>
</td>
<td>
(Recommended reading, but not required: D. Jurafsky, J.H. Martin. Chp. <a href="https://web.stanford.edu/~jurafsky/slp3/4.pdf" target="_blank">4</a>)
</td>
</tr>
<tr>
<td>7/11/2025</td>
<td>
Machine Learning for Text Analytics
</td>
<td>
<a href="https://drive.google.com/file/d/160HDXsBjSTR7Fr14LA-so2DPXJEsSYJv/view?usp=drive_link" target="_blank"> 12 - Machine Learning for Text Analytics (part. 2) (correct version)</a>
</td>
<td>
Notebook <a href="https://drive.google.com/file/d/1ei_XGnzH18MOAtkIrfDe-_crtZDoiXLA/view?usp=drive_link" target="_blank">Supervised Classification </a>
</td>
</tr>
<tr>
<td>13/11/2024</td>
<td>
Student project presentations
</td>
<td>
<a href="https://drive.google.com/file/d/1WNs9K3v7MoC4CIeQ8kkAgU06w6yu0Tdm/view?usp=drive_link" target="_blank"> First presentation: How to</a>
</td>
<td>
</td>
</tr>
<tr>
<td>14/11/2025</td>
<td>
Topic Modeling
</td>
<td>
<a href="https://drive.google.com/file/d/1AoUuBTsBx5VFOjUv70kjBIeGpIFPjuIm/view?usp=drive_link" target="_blank"> 13 - Topic Modeling</a>
</td>
<td>
<ul>
<li>
Zhai and Massung (2016) Text Data Management and Analysis. Ch. <a href="https://drive.google.com/file/d/1BMQrlHMGrPk9-e3MkZZfjKqZSw8QLM8k/view?usp=drive_link" target="_blank"> 17</a>.
</li>
<li>
Notebooks <a href="https://drive.google.com/file/d/1RTxpGOWY7OEJfmv_mwE87uJ6xM-VMb4D/view?usp=drive_link" target="_blank">Topic Modeling (Gensim & Sklearn)</a>
</li>
</ul>
</td>
</tr>
<tr>
<td>20/11/2025</td>
<td>
Topic Modeling and Optimization
</td>
<td>
<i>13.1 - Practice on Topic Modeling and Optimization</i>
</td>
<td>
Notebook <a href="https://drive.google.com/file/d/1-B58a4sZ7nBtPj7Gsfe13qt4u72LGzzQ/view?usp=drive_link" target="_blank">Optimization</a>
</td>
</tr>
<tr>
<td>21/11/2025</td>
<td>
A primer on Neural Networks
</td>
<td>
<a href="https://drive.google.com/file/d/1d9_j7n5i7Leq5zCJ_tzyCOBBtlcbYWWF/view?usp=drive_link" target="_blank"> 14 - A primer on Neural Networks</a>
</td>
<td>
<ul>
<li>
<a href="https://drive.google.com/file/d/1_5-WZrqNEqrD7zIJpJkqsETtZHOmk2kS/view?usp=drive_link" target="_blank">Forward and backward pass in a neural network</a> by Andrea Esuli (September 3, 2020)
</li>
<li>
Helper resource (optional): <a href="http://neuralnetworksanddeeplearning.com/" target="_blank">Neural Networks and Deep Learning</a> by Michael Nielsen (2019)
</li>
</ul>
</td>
</tr>
<tr>
<td>27/11/2025</td>
<td>
NNs Types and Characteristics
</td>
<td>
<a href="https://drive.google.com/file/d/1g3h0nUSaVrQ7iDG57o8kUox8cVF23FpU/view?usp=drive_link" target="_blank">15 - NNs Types and Characteristics</a>
</td>
<td>
Notebooks
<ul>
<li>
<a href="https://drive.google.com/file/d/1ImJwV-Wg9aghPIKcyI8PgAdyd350eAnw/view?usp=drive_link" target="_blank">From SVM to NN</a>
</li>
<li>
<a href="https://drive.google.com/file/d/16lzRKa_hBrqK5zI5nqPZr0D8yICmzOkt/view?usp=drive_link" target="_blank">Classification - cnnNet</a>
</li>
</ul>
</td>
</tr>
<tr>
<td>28/11/2025</td>
<td>
(Static) Embeddings
</td>
<td>
<a href="https://drive.google.com/file/d/1u_tD_bealHam65IlP_qIY36Q2KzeeiGO/view?usp=drive_link" target="_blank">16 - (Static) Embeddings</a>
</td>
<td>
<ul>
<li>
Notebook <a href="https://drive.google.com/file/d/14X_O5yazY3BtTfwLtU23wmqO-LaU_Ehs/view?usp=drive_link" target="_blank">Classification - LSTMNet</a>
</li>
<li>
D. Jurafsky, J.H. Martin. Chps. <a href="https://drive.google.com/file/d/1uSibXrVj8IFTXBuA7PadfGkuIDy1GQ1C/view?usp=drive_link" target="_blank">7</a>,
<a href="https://drive.google.com/file/d/19ueOYGCtZYBNuRM7bIiQ9UsDb4t92coP/view?usp=drive_link" target="_blank">13</a>,
<a href="https://drive.google.com/file/d/1x7FsBe5Gfd1fi8iDT_EWbKXLY60L8zLL/view?usp=drive_link" target="_blank">5</a>
</li>
</ul>
</td>
</tr>
<tr>
<td>4/12/2025</td>
<td>
Student project presentations
</td>
<td>
<a href="https://drive.google.com/file/d/1gek2wVQHDkTGvtY3ieabpZCFF0H4wd8H/view?usp=sharing" target="_blank"> Second presentation: How to</a>
</td>
<td>
</td>
</tr>
<tr>
<td>5/12/2025</td>
<td>
Contextual Embeddings & Neural Language Models
</td>
<td>
<a href="https://drive.google.com/file/d/1hpdGe7lOhnUuKcf7LtuJxU5ctnKQf1wv/view?usp=sharing" target="_blank">17 - Contextual Embeddings & Neural Language Models</a>
</td>
<td>
<ul>
<li>
D. Jurafsky, J.H. Martin. Chp. <a href="https://drive.google.com/file/d/1jb_RwBAFCwmtM3d3eXrmhfvr4LL4pgsP/view?usp=drive_link" target="_blank">8</a>,
</li>
<li>
Notebook <a href="https://drive.google.com/file/d/1yLwcTRDb76Q69nwH4L6OGjItacRijH9d/view?usp=drive_link" target="_blank">Word2Vec & FastText</a>
</li>
</ul>
</td>
</tr>
<tr>
<td>9/12/2025</td>
<td>
Neural Language Models & Transformer
</td>
<td>
<a href="https://drive.google.com/file/d/109MdNeEiP12O7EuZMBay1FATLxM7W3fk/view?usp=drive_link" target="_blank">18 - Neural Language Models - Transformer</a>
</td>
<td>
D. Jurafsky, J.H. Martin. Chp. <a href="https://drive.google.com/file/d/13IGjKbHdLaw4obdLOpHXpsiBNFQA28TP/view?usp=drive_link" target="_blank">11</a>,
</td>
</tr>
<tr>
<td>11/12/2025</td>
<td>
Dialogue Systems
</td>
<td>
<a href="https://drive.google.com/file/d/1oG8QZYL4VyBQE8M2mkvBEG532w-bYsEX/view?usp=drive_link" target="_blank">19 - Dialogue Systems</a>
</td>
<td>
<ul>
<li>
D. Jurafsky, J.H. Martin. Chps. <a href="https://drive.google.com/file/d/1XVjSEMJ8WU-M0iLyP1RlddWRD1SdjZh6/view?usp=drive_link" target="_blank">24</a>
</li>
<li>
Notebook <a href="https://drive.google.com/file/d/1Iy36NjAhQl-cjyuPOTFMiOduCt-e3y_C/view?usp=drive_link" target="_blank">Chatbot</a>
</li>
<li>
Notebook <a href="https://drive.google.com/file/d/1wtko2gwT9jV6XRLzqyn5Is-0dFUw8InN/view?usp=drive_link" target="_blank">Task-Oriented Chatbot</a>
</li>
</ul>
</td>
</tr>
<tr>
<td>12/12/2025</td>
<td>
Practice on BERT - Instructions for Final Exam [Teacher present, but classrooms closed due to strike]
</td>
<td>
<a href="https://drive.google.com/file/d/1xC6xSxzQWGN6Rt7M2zNuqcp3tg98TKi5/view?usp=drive_link" target="_blank">Final Exam: How to</a>
</td>
<td>
<ul>
<li>
Notebook <a href="https://drive.google.com/file/d/18B1eZpOUi1de0ORdZnhxY4sP3ZX3ai85/view?usp=drive_link" target="_blank">Bert (Binary)</a>
</li>
<li>
Notebook <a href="https://drive.google.com/file/d/1eLDWXOfsz9zOxi4W21x485XC2zL388vO/view?usp=drive_link" target="_blank">Bert (Multi-class)</a>
</li>
</ul>
</td>
</tr>
</tbody>
</table>
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