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mds:txa:start [02/02/2021 alle 09:17 (3 anni fa)]
Andrea Esuli [Exam]
mds:txa:start [05/11/2021 alle 09:43 (2 anni fa)]
Andrea Esuli [Lecture Notes]
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-====== Text Analytics A.Y. 2020/21 ======+====== Text Analytics (635AA) A.Y. 2021/22 ======
  
  
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 ==== Schedule  ==== ==== Schedule  ====
  
-Lectures will be given using Microsoft Teams. +^ Day ^ Hour ^ Room ^  
-[[https://teams.microsoft.com/l/team/19%3ad515c158b0c64bc8b4efa3b21aab6fa7%40thread.tacv2/conversations?groupId=205782a2-623f-4f50-a391-9d4f22d4d604&tenantId=c7456b31-a220-47f5-be52-473828670aa1|Join the Text Analytics Team here.]] +Monday | 9-11 | Fib C - [[https://teams.microsoft.com/l/channel/19%3aPNQvMI4MdxtWb0_5d1r1UoIPA9QHxRe6kOuZ9VHTG-I1%40thread.tacv2/General?groupId=109c5615-d2be-49ef-848f-85c7aa07de5b&tenantId=c7456b31-a220-47f5-be52-473828670aa1|Teams]]| 
- +Friday| 11-13 Fib M1 -  [[https://teams.microsoft.com/l/channel/19%3aPNQvMI4MdxtWb0_5d1r1UoIPA9QHxRe6kOuZ9VHTG-I1%40thread.tacv2/General?groupId=109c5615-d2be-49ef-848f-85c7aa07de5b&tenantId=c7456b31-a220-47f5-be52-473828670aa1|Teams]]|
-Lecture recording is available on Microsoft Teams for delayed viewing. +
- +
-^ Day ^ Hour ^ Room ^ +
-Wednesday | 9-11 | [[https://teams.microsoft.com/l/team/19%3ad515c158b0c64bc8b4efa3b21aab6fa7%40thread.tacv2/conversations?groupId=205782a2-623f-4f50-a391-9d4f22d4d604&tenantId=c7456b31-a220-47f5-be52-473828670aa1|Text Analytics Team]] | +
-Thursday 9-11 | [[https://teams.microsoft.com/l/team/19%3ad515c158b0c64bc8b4efa3b21aab6fa7%40thread.tacv2/conversations?groupId=205782a2-623f-4f50-a391-9d4f22d4d604&tenantId=c7456b31-a220-47f5-be52-473828670aa1|Text Analytics Team]] |+
  
  
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 ==== Exam ==== ==== Exam ====
 +
 +**__Students MUST contact the teacher at least one month before the date set for the exam session__, so as to agree on the contents of the project and get a go ahead.**
 +
 +**The date set for the exam session ([[https://esami.unipi.it/esami/findcourse.php?id=52426|Check here]]) is the deadline for submitting the completed project (report and code).**
  
 Exam will consist in a project to be agreed with the teacher and an oral exam. Exam will consist in a project to be agreed with the teacher and an oral exam.
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 The purpose of the project is to let you have some hands on experience on applying the concepts and methods seen during the course to practical text analytics problems. The purpose of the project is to let you have some hands on experience on applying the concepts and methods seen during the course to practical text analytics problems.
  
-Projects may be based on challenges proposed in either research forums ([[https://alt.qcri.org/semeval2020/|Semeval]], [[http://www.evalita.it/|Evalita]]) or other platforms ([[https://kaggle.com|Kaggle]]). Students are also invited to propose their on problem based on other sources (e.g., recent papers on ArXiv [[https://arxiv.org/list/cs.CL/new|CL]] or [[https://arxiv.org/list/cs.AI/new|AI]]), or their own interests.+Projects may be based on challenges proposed in either research forums ([[https://alt.qcri.org/semeval2020/|Semeval]], [[http://www.evalita.it/|Evalita]]) or other platforms ([[https://kaggle.com|Kaggle]]). Students are also invited to propose a project on problem based on other sources (e.g., recent papers on ArXiv [[https://arxiv.org/list/cs.CL/new|CL]] or [[https://arxiv.org/list/cs.AI/new|AI]]), or their own interests.
  
 Students may work solo or in groups up to three persons. Students may work solo or in groups up to three persons.
- 
-Before starting working on the project students must contact the teacher so as to agree on the contents of the project and get a go ahead. 
  
 ==== Lecture Notes ==== ==== Lecture Notes ====
  
 ^ Date ^ Lecture ^ Notes ^ ^ Date ^ Lecture ^ Notes ^
-2020/09/16 | Introduction to the course | {{ :mds:txa:00_-_introduction_to_the_text_analytics_course.pdf |}} {{ :mds:txa:01_-_natural_language_and_text_analytics.pdf |}} | +2021/09/13 | Introduction to the course, NLP & Text Analytics | {{ :mds:txa:00_-_introduction_to_the_text_analytics_course.pdf |}}{{ :mds:txa:01_-_natural_language_and_text_analytics.pdf |}} | 
-2020/09/17 | Introduction to probability | {{ :mds:txa:02_-_introduction_to_probability.pdf |}} | +2021/09/17 | Introduction to probability | {{ :mds:txa:02_-_introduction_to_probability.pdf |}}  |  
-2020/09/23 Setup of Python environment | {{ :mds:txa:03_-_introduction_to_python.pdf |}} +2021/09/20 //canceled// |  
-| 2020/09/24 | Introduction to Python | {{ :mds:txa:03_1_introduction_to_python.zip |}} | +| 2021/09/24 | Introduction to python 1/2 | {{ :mds:txa:03_-_introduction_to_python.pdf |}} {{ :mds:txa:03_1_introduction_to_python.zip |}}|  
-2020/09/30 | Probabilistic Language Models | {{ :mds:txa:04_-_probabilistic_language_models.pdf |}} +2021/09/27 | Introduction to python 2/2 |  |  
-| 2020/10/01 | Probabilistic Language Models | {{ :mds:txa:04_1_probabilisticlanguagemodel.zip |}} | +| 2021/10/01 | Probabilistic Language Models 1/2 | {{ :mds:txa:04_-_probabilistic_language_models.pdf |}} {{ :mds:txa:04_1_probabilisticlanguagemodel.zip |}}|  
-2020/10/07 | Text IndexingRegular expressions | {{ :mds:txa:05_-_text_indexing.pdf |}} {{ :mds:txa:05.1_-_strings_regular_expressions_and_bs4.zip |}} | +2021/10/04 | Probabilistic Language Models 2/2 |  | 
-2020/10/08 | NLTK, Collocations | {{ :mds:txa:05.2_-_nltk.zip |}} {{ :mds:txa:05.3_-_collocations.zip |}} |  +| 2021/10/08 | Text IndexingRegular expressions | {{ :mds:txa:05_-_text_indexing.pdf |}} {{ :mds:txa:05_1_strings_regular_expressions_and_bs4.zip |}}  
-2020/10/14 NLP tools, Spacy, Text indexingpreprocessing | {{ :mds:txa:05.4_-_spacy_text_processing.ipynb.zip |}} |  +2021/10/11 Text Indexing: NLTK, Collocations | {{ :mds:txa:05_2_nltk.zip |}} {{ :mds:txa:05_3_collocations.zip |}} | 
-2020/10/15 Vector space model, ML for text analytics | {{ :mds:txa:06_-_machine_learning_for_text_analytics.pdf |}} |  +2021/10/15 Text Indexing: Spacy, Feature selectionPipeline | {{ :mds:txa:05_4_spacy_text_processing.zip |}}  
-| 2020/10/21 | Scikit learn, pipeline | {{ :mds:txa:06_1_classification_sklearn.zip |}} |  +2021/10/18 Text Indexing: Notebook. Introduction to Machine Learning | {{ :mds:txa:05_5_text_indexing_sklearn.zip |}} {{ :mds:txa:06_-_machine_learning_for_text_analytics.pdf |}}| 
-2020/10/22 | Feature engineering | {{ :mds:txa:06_2_classification_feature_engineering.zip |}} |  +2021/10/22 | Machine Learning for TA: Paradigms and models  {{ :mds:txa:06_-_machine_learning_for_text_analytics.pdf |}} | 
-2020/10/28 Experimental protocols, optimization | {{ :mds:txa:07_-_experiments.pdf |}} {{ :mds:txa:07_1_optimization_sklearn.zip |}}|  +2021/10/25 Machine Learning for TAthe complete pipeline | {{ :mds:txa:06_1_classification_sklearn.zip |}} | 
-2020/10/29 | Sequence labeling, information extraction | {{ :mds:txa:08_-_information_extraction.pdf |}}  |  +2021/10/29 | Machine Learning for TAFeature engineeringTopic Modeling | {{ :mds:txa:06_2_classification_feature_engineering.zip |}} {{ :mds:txa:06.3_-_topic_modeling.pdf |}} {{ :mds:txa:06.4_-_topic_modeling.ipynb.zip |}} | 
-| 2020/11/04 | Inceptionspacy | {{ :mds:txa:08_1_spacy_ner_train.zip |}} |  +2022/11/05 Experimental protocols and optimization | {{ :mds:txa:07_-_experiments.pdf |}} {{ :mds:txa:07_1_optimization_sklearn.zip |}} | 
-| 2020/11/05 | Data collection | {{ :mds:txa:09_-_data_collection.pdf |}} {{ :mds:txa:09_1_scraping.zip |}} {{ :mds:txa:09_2_data_from_twitter.zip |}} | +2022/11/08   
-2020/11/11 Introduction to neural networks | {{ :mds:txa:10_-_a_primer_on_neural_networks.pdf |}} {{ :mds:txa:10.1_-_example_of_backpropagation.pdf |}}|  +2022/11/12   
-2020/11/12 From SVM to NN, deep learning {{ :mds:txa:10_2_svm_to_nn.zip |}} |  +2022/11/15   
-2020/11/18 Convolutional and Recurrent networks, text generation {{ :mds:txa:10_3_classification_cnnnet.zip |}} {{ :mds:txa:10_4_classification_lstmnet.zip |}} {{ :mds:txa:10_5_textgeneration.zip |}}|  +2022/11/19   
-2020/11/19 Word embeddings, neural language models {{ :mds:txa:11_-_neural_language_models.pdf |}} {{ :mds:txa:11_1_wordembeddings.zip |}}|  +2022/11/22   
-2020/11/25 Document embeddings, the Transformer {{ :mds:txa:11_2_documentembeddings.zip |}} |  +2022/11/26   
-2020/11/26 BERT fine-tuning {{ :mds:txa:11_3_bert_finetune_binary.zip |}} {{ :mds:txa:11_4_bert_finetune_multiclass.zip |}} {{ :mds:txa:11_5_simpletransformers_finetune_binary.zip |}} {{ :mds:txa:11_6_simpletransformer_generation_and_representation.zip |}}|  +2022/11/29   
-|2020/12/Parsing {{ :mds:txa:12_-_parsing-1.pptx |}} |  +2022/12/03   |
-|2020/12/Parsing {{ :mds:txa:12_-_parsing-2.pptx |}} |  +
-|2020/12/Introduction to Sentiment Analysis, Sentiment Lexicons {{ :mds:txa:13_-_sentiment_analysis.pdf |}} {{ :mds:txa:14_-_lexical_resources_for_sentiment_analysis.pdf |}} | +
-|2020/12/10| Sentiment Classification | {{ :mds:txa:15_-_sentiment_classification.pdf |}} {{ :mds:txa:15_1_vader.zip |}} +
  
 ==== Textbooks ==== ==== Textbooks ====
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 ==== Previous editions ==== ==== Previous editions ====
  
 +  * [[http://didawiki.di.unipi.it/doku.php/mds/txa/start?rev=1612257498|2020-2021]]
   * [[https://elearning.di.unipi.it/course/view.php?id=162|2019-2020]]   * [[https://elearning.di.unipi.it/course/view.php?id=162|2019-2020]]
   * [[http://didawiki.di.unipi.it/doku.php/mds/txa/start?rev=1551450538|2018-2019]]   * [[http://didawiki.di.unipi.it/doku.php/mds/txa/start?rev=1551450538|2018-2019]]
   * [[http://didawiki.di.unipi.it/doku.php/mds/txa/start?rev=1515682954|2017-2018]]   * [[http://didawiki.di.unipi.it/doku.php/mds/txa/start?rev=1515682954|2017-2018]]
  
mds/txa/start.txt · Ultima modifica: 15/01/2024 alle 10:31 (2 mesi fa) da Laura Pollacci