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Indice
Text Analytics A.Y. 2018/19
Teachers
Schedule | ||
---|---|---|
Day | Hour | Room |
Monday | 11-13 | X1, Polo Fibonacci |
Tuesday | 9-11 | X1, Polo Fibonacci |
Forum
Forum on Piazza
Objectives
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 objective is to learn to recognize situations in which text analytics techniques can solve information processing needs, to identify the analytic task/process that best models the business problem, to select the most appropriate resources methods and tools, to collect text data and apply such methods to them. Several applications context will be presented: information extraction, sentiment analysis (what is the nature of commentary on an issue), spam and fake posts detection, quantification problems, summarization, etc.
- Disciplinary background: Natural Language Processing, Information Retrieval and Machine Learning
- Mathematical background: Probability, Statistics and Algebra
- Linguistic essentials: words, lemmas, morphology, PoS, syntax
- Basic text processing: regular expression, tokenisation
- Data gathering: twitter API, scraping
- Basic modelling: collocations, language models
- Introduction to Machine Learning: theory and practical tips
- Libraries and tools: NLTK, Keras
- Applications:
- Classification/Clustering
- Sentiment Analysis/Opinion Mining
- Information Extraction/Relation Extraction
- Entity Linking
- Spam Detection: mail spam & phishing, blog spam, review spam
Jupyter Notebook Server
A server has been setup for running Jupyter Notebooks. In order to log into the server, you must get credentials for a Google Suite account:go to this page and register with your University credentials to activate your free account.
Lecture Notes
Date | Lecture | Notes |
---|---|---|
17/9/2018 | Introduction | Text Analytics |
18/9/2018 | Introduction to Probability | Probability |
24/9/2018 | Language Modeling | Language Modeling |
25/9/2018 | Introduction to Python | See notebooks “Introduction to Python” in folder “Text Analytics” on http://attardi-4.di.unipi.it:8000/“ |
1/10/2018 | Introduction to Python | See notebooks “Introduction to Python 2” and “RegEx” in folder “Text Analytics” on http://attardi-4.di.unipi.it:8000/“ |
2/10/2018 | Introduction to NLTK | See notebooks “Introduction to NLTK” in folder “Text Analytics” on http://attardi-4.di.unipi.it:8000/“ |
8/10/2018 | Preprocessing and tokenization | Tokenization |
9/10/2018 | Word Similarity | Tokenization Homework 1 (deadline 15/10) |
15/10/2018 | Correction of Homework 1, Text Classification | Text Classification |
16/10/2018 | Classifiers | Classifiers |
22/10/2018 | Hidden Markov Models | HMM |
23/10/2018 | POS Tagging | HMM |
29/10/2018 | HomeWork 2 | |
5/11/2018 | Named Entity Tagging | NER |
13/11/2018 | Neural Language Models: PCA, Word2Vec | LM See notebooks “LanguageModels.ipynb” on http://attardi-4.di.unipi.it:8000/” |
20/11/2018 | NLM: FastText, Doc2Vec | LM See notebooks “docEmbeddings.ipynb” on http://attardi-4.di.unipi.it:8000/” |
Textbooks
- D. Jurafsky, J.H. Martin, Speech and Language Processing. 3nd edition, Prentice-Hall, 2018.
- B. Liu, Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, 2012.
- S. Bird, E. Klein, E. Loper. Natural Language Processing with Python.