NLTK Training in Jersey, CI

NLTK Training in Jersey, CI

Python Natural Language Toolkit

St Helier, Jersey, Channel Isles

Digital Jersey Hub
Block 3, The Forum, Grenville Street
St Helier JE2 4UF
Jersey
JE
St Helier, Jersey, Channel Isles
Business Hours Monday-Friday: 8:30am – 6pm Phone 01534 828 585 01534 828 587 Facilities: The Hub is equipped with free Gigabit Wi-Fi. Breakout Zone This is a...Read more

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Natural Language Processing with Python

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Course Outlines

Code Name Duration Outline
python_nltk Natural Language Processing with Python 28 hours This course introduces linguists or programmers to NLP in Python. During this course we will mostly use nltk.org (Natural Language Tool Kit), but also we will use other libraries relevant and useful for NLP. At the moment we can conduct this course in Python 2.x or Python 3.x. Examples are in English or Mandarin (普通话). Other languages can be also made available if agreed before booking.

Overview of Python packages related to NLP

 

Introduction to NLP (examples in Python of course)

  1. Simple Text Manipulation
    1. Searching Text
    2. Counting Words
    3. Splitting Texts into Words
    4. Lexical dispersion
  2. Processing complex structures
    1. Representing text in Lists
    2. Indexing Lists
    3. Collocations
    4. Bigrams
    5. Frequency Distributions
    6. Conditionals with Words
    7. Comparing Words (startswith, endswith, islower, isalpha, etc...)
  3. Natural Language Understanding
    1. Word Sense Disambiguation
    2. Pronoun Resolution
  4. Machine translations (statistical, rule based, literal, etc...)
  5. Exercises

NLP in Python in examples

  1. Accessing Text Corpora and Lexical Resources
    1. Common sources for corpora
    2. Conditional Frequency Distributions
    3. Counting Words by Genre
    4. Creating own corpus
    5. Pronouncing Dictionary
    6. Shoebox and Toolbox Lexicons
    7. Senses and Synonyms
    8. Hierarchies
    9. Lexical Relations: Meronyms, Holonyms
    10. Semantic Similarity
  2. Processing Raw Text
    1. Priting
    2. struncating
    3. extracting parts of string
    4. accessing individual charaters
    5. searching, replacing, spliting, joining, indexing, etc...
    6. using regular expressions
    7. detecting word patterns
    8. stemming
    9. tokenization
    10. normalization of text
    11. Word Segmentation (especially in Chinese)
  3. Categorizing and Tagging Words
    1. Tagged Corpora
    2. Tagged Tokens
    3. Part-of-Speech Tagset
    4. Python Dictionaries
    5. Words to Propertieis mapping
    6. Automatic Tagging
    7. Determining the Category of a Word (Morphological, Syntactic, Semantic)
  4. Text Classification (Machine Learning)
    1. Supervised Classification
    2. Sentence Segmentation
    3. Cross Validation
    4. Decision Trees
  5. Extracting Information from Text
    1. Chunking
    2. Chinking
    3. Tags vs Trees
  6. Analyzing Sentence Structure
    1. Context Free Grammar
    2. Parsers
  7. Building Feature Based Grammars
    1. Grammatical Features
    2. Processing Feature Structures
  8. Analyzing the Meaning of Sentences
    1. Semantics and Logic
    2. Propositional Logic
    3. First-Order Logic
    4. Discourse Semantics
  9.  Managing Linguistic Data 
    1. Data Formats (Lexicon vs Text)
    2. Metadata
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