Word in Beddings

How do you process and classify text documents in Python? What are the fundamental techniques and building blocks for Natural Language Processing (NLP)? This week on the show, Jodie Burchell, developer advocate for data science at JetBrains, talks about how machine learning (ML) models understand text.

Jodie explains how ML models require data in a structured format, which involves transforming text documents into columns and rows. She covers the most straightforward approach, called binary vectorization. We discuss the bag-of-words method and the tools of stemming, lemmatization, and count vectorization.

We jump into word embedding models next. Jodie talks about WordNet, Natural Language Toolkit (NLTK), word2vec, and Gensim. Our conversation lays a foundation for starting with text classification, implementing sentiment analysis, and building projects using these tools. Jodie also shares multiple resources to help you continue exploring NLP and modeling.

Topics:

• 00:00:00 – Introduction
• 00:02:47 – Exploring the topic
• 00:06:00 – Perceived sentience of LaMDA
• 00:10:24 – How do we get started?
• 00:11:16 – What are classification and sentiment analysis?
• 00:13:03 – Transforming text in rows and columns
• 00:14:47 – Sponsor: Snyk
• 00:15:27 – Bag-of-words approach
• 00:19:12 – Stemming and lemmatization
• 00:22:05 – Capturing N-grams
• 00:25:34 – Count vectorization
• 00:27:14 – Stop words
• 00:28:46 – Text Frequency / Inverse Document Frequency (TFIDF) vectorization
• 00:32:28 – Potential projects for bag-of-words techniques
• 00:34:07 – Video Course Spotlight
• 00:35:20 – WordNet and NLTK package
• 00:37:27 – Word embeddings and word2vec
• 00:45:30 – Previous training and too many dimensions
• 00:50:07 – How to use word2vec and Gensim?
• 00:51:26 – What types of projects for word2vec and Gensim?
• 00:54:41 – Getting into GPT and BERT in another episode
• 00:56:11 – How to follow Jodie’s work?
• 00:57:36 – Thanks and goodbye

Show Links:

• Why Google’s “sentient” AI LaMDA is nothing like a person.
• On NYT Magazine on AI: Resist the Urge to be Impressed | Emily M. Bender | Medium
• ELIZA - Wikipedia
• eliza.py - Python 2 version by Daniel Connelly
• dabraude/Pyliza: Python3 Implementation of Eliza
• magneticpoetry.com
• Natural Language Processing With Python’s NLTK Package – Real Python
• Practical Text Classification With Python and Keras – Real Python
• Sentiment Analysis: First Steps With Python’s NLTK Library – Real Python
• NLTK: Natural Language Toolkit
• spaCy · Industrial-strength Natural Language Processing in Python
• Natural Language Processing With spaCy in Python - Real Python
• Stemming - Wikipedia
• Lemmatization - Wikipedia
• Binary/Count Vectorization: sklearn.feature_extraction.text.CountVectorizer— scikit-learn
• TFIDF: sklearn.feature_extraction.text.TfidfVectorizer — scikit-learn
• Porter Stemmer: nltk.stem.porter module — NLTK
• Snowball Stemmer: nltk.stem.snowball module — NLTK
• WordNet Lemmatizer: nltk.stem.wordnet module — NLTK
• Lemmatizer · spaCy API Documentation
• Applying Bag of Words and Word2Vec models on Reuters-21578 Dataset Elvin Ouyang’s Blog
• UCI Machine Learning Repository: Reuters-21578 Text Categorization Collection Data Set
• The Illustrated Word2vec – Jay Alammar
• A Complete Guide to Using WordNET in NLP Applications
• Gensim: Topic modeling for humans
• Core Tutorials — gensim
• Find Open Datasets and Machine Learning Projects | Kaggle
• Engineering All Hands: Vectorise all the things! - YouTube
• PyCon Portugal 2022
• NDC Oslo 2022 | Conference for Software Developers
• Jodie Burchell’s Blog - Standard error
• Jodie Burchell 🇦🇺🇩🇪 (@t_redactyl) / Twitter
• JetBrains: Essential tools for software developers and teams

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