Natural Language Processing with Python Training Course

Saigon Tower, 29 Lê Duẩn, Bến Nghé, District 1, Ho Chi Minh City, Vietnam

Description

Overview

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.

Course Details :

Course Code : python_nltk
Duration: 28 hours (usually 4 days including breaks)
Workday courses take place between 09:30 and 16:30
Requirements
– Basic Knowledge of Python
Fees : 479477754 VND(Price per participant)
Venue : Ho Chi Minh City, Saigon Tower, Vietnam

Course Outline

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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