Analytics Vidhya's Datahack 2019 Applied Natural Language Processing Workshop

Repository of contents (+notebooks) covered in AV's Datahack Summit 2019 workshop on Applied NLP by Sudalai Rajkumar

Workshop: Applied Natural Language Processing - Sudalai Rajkumar (SRK)

Structure of the Workshop

1. Introduction to Natural Language Processing
2. Text pre-processing and Wrangling
   • Removing HTML tagsnoise
   • Removing accented characters
   • Removing special characterssymbols
   • Handling contractions
   • Stemming
   • Lemmatization
   • Stop word removal
3. Project: Build a duplicate character removal module
4. Project: Build a spell-check and correction module
5. Project: Build an end-to-end text pre-processor
6. Text Understanding
   • POS (Parts of Speech) Tagging
   • Text Parsing
     • Shallow Parsing
     • Dependency Parsing
     • Constituency Parsing
   • NER (Named Entity Recognition) Tagging
7. Project: Build your own POS Tagger
8. Project: Build your own NER Tagger
9. Text Representation – Feature Engineering
   • Traditional Statistical Models – BOW, TF-IDF
   • Newer Deep Learning Models for word embeddings – Word2Vec, GloVe, FastText
10. Project: Similarity and Movie Recommendations
11. Project: Interactive exploration of Word Embeddings
12. Case Studies for other common NLP Tasks
    • Project: Sentiment Analysis using unsupervised learning and supervised learning (machine and deep learning)
    • Project: Text Clustering (grouping similar movies)
    • Project: Text Summarization and Topic Models
13. Promise of Deep Learning for NLP, Transfer and Generative Learning
14. Final words and where to go from here?

Key Takeaways

• Learn and understand popular NLP workflows with interactive examples
• Covers concepts and interactive projects on cleaning and handling noisy unstructured text data including duplicate checks, spelling corrections and text wrangling
• Build your own POS and NER taggers and parse text data to understand it better
• Understand, build and explore text semantics and representations with traditional statistical models and newer word embedding models
• Projects on popular NLP tasks including text classification, sentiment analysis, text clustering, summarization, topic models and recommendations
• Brief coverage of the promise of deep learning for NLP

Weblink of the workshop: https://www.analyticsvidhya.com/datahack-summit-2019/schedule/workshop-applied-natural-language-processing/