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Natural Language Processing

This is a set of code used to demonstrate basics of natural language processing with some basic of machine learning.

Notebooks

The notebooks contain code to demonstrate the concepts and how they are applied in code and data.

Regular Expressions

This shows how to use the re module in python as well as some examples of the different Regular Expression (Regex) syntax.

Main Concepts:

  • Using Python's re module
  • Matching multiple cases with Regex's disjunction
  • Reusing the same pattern with Regex's quantifiers
  • Positioning the matches with Regex's anchors
  • Retrieving matches with Regex's capture groups
  • Looking before and after a match with Regex's lookahead and lookbehind

Text Normalization

This notebook demonstrate the process of converting text into a standard form. This is one of the first step in the pipelines for a natural language processing project.

Main Concepts:

  • Exploring datasets using Regex
  • Splitting sentences and paragraphs to words
  • Transforming words into roots and base forms
  • Separating different sentences from a paragraph

Part of Speech Tagging

This notebook contains code to show how words in the sentences are given their sentence function, or their part of speech (POS).

Main Concepts:

  • Utilizing word order using N-Gram taggers
  • Training different machine learning algorithms to develop a POS tagger

Word Classification

This notebook implements some basic pipelines for processing different words for classification. Specifically, this contains a basic sentiment analysis pipeline.

Main Concepts:

  • Balancing imbalance datasets
  • Creating word features for training models
  • Using a Naive Bayes algorithm for sentiment analysis
  • Interpreting results of a Naive Bayes algorithm

Document Classification

This notebook extends the concepts in Word Classification. This creates a document classification pipeline by determining what topic a paragraph is discussing.

Main Concepts:

  • Creating paragraph features for training models such as counts and tf-idf
  • Using a Random Forest algorithm for document classification
  • Interpreting results of a Random Forest algorithm

Information Extraction

This notebook contains topics related to Chunking and Named Entity Recognition (NER). These are two concepts are often used for information extraction within a text.

Main Concepts:

  • Working with sentence syntax tree formats
  • Creating syntax trees using rule based approaches
  • Training syntax tree models using different machine learning algorithms
  • Developing a NER model using Conditional Random Fields (CRF)

Models

The models folder is used as the output of the trained models when running the notebook. Specifically, the CRF models created by NLTK are stored here.