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Course Outline
Overview of Python packages related to NLP
Introduction to NLP (examples in Python of course)
- Simple Text Manipulation
- Searching Text
- Counting Words
- Splitting Texts into Words
- Lexical dispersion
- Processing complex structures
- Representing text in Lists
- Indexing Lists
- Collocations
- Bigrams
- Frequency Distributions
- Conditionals with Words
- Comparing Words (startswith, endswith, islower, isalpha, etc...)
- Natural Language Understanding
- Word Sense Disambiguation
- Pronoun Resolution
- Machine translations (statistical, rule based, literal, etc...)
- Exercises
NLP in Python in examples
- Accessing Text Corpora and Lexical Resources
- Common sources for corpora
- Conditional Frequency Distributions
- Counting Words by Genre
- Creating own corpus
- Pronouncing Dictionary
- Shoebox and Toolbox Lexicons
- Senses and Synonyms
- Hierarchies
- Lexical Relations: Meronyms, Holonyms
- Semantic Similarity
- Processing Raw Text
- Priting
- Struncating
- Extracting parts of string
- Accessing individual charaters
- Searching, replacing, spliting, joining, indexing, etc...
- Using regular expressions
- Detecting word patterns
- Stemming
- Tokenization
- Normalization of text
- Word Segmentation (especially in Chinese)
- Categorizing and Tagging Words
- Tagged Corpora
- Tagged Tokens
- Part-of-Speech Tagset
- Python Dictionaries
- Words to Propertieis mapping
- Automatic Tagging
- Determining the Category of a Word (Morphological, Syntactic, Semantic)
- Text Classification (Machine Learning)
- Supervised Classification
- Sentence Segmentation
- Cross Validation
- Decision Trees
- Extracting Information from Text
- Chunking
- Chinking
- Tags vs Trees
- Analyzing Sentence Structure
- Context Free Grammar
- Parsers
- Building Feature Based Grammars
- Grammatical Features
- Processing Feature Structures
- Analyzing the Meaning of Sentences
- Semantics and Logic
- Propositional Logic
- First-Order Logic
- Discourse Semantics
- Managing Linguistic Data
- Data Formats (Lexicon vs Text)
- Metadata
Requirements
Basic Knowledge of Python
28 Hours
Testimonials (4)
Examples/exercices perfectly adapted to our domain
Luc - CS Group
Course - Scaling Data Analysis with Python and Dask
The fact of having more practical exercises using more similar data to what we use in our projects (satellite images in raster format)
Matthieu - CS Group
Course - Scaling Data Analysis with Python and Dask
The trainer was very available to answer all te kind of question I did
Caterina - Stamtech
Course - Developing APIs with Python and FastAPI
Transfer of practical knowledge and experience of the trainer.
Rumel Mateusz - Pojazdy Szynowe PESA Bydgoszcz SA
Machine Translated