MLG 019 Natural Language Processing 2

Machine Learning Guide

Content provided by OCDevel. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by OCDevel or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

8y ago 1:05:54

MP3•Episode home

Try a walking desk to stay healthy while you study or work!

Notes and resources at ocdevel.com/mlg/19

Classical NLP Techniques:

Origins and Phases in NLP History: Initially reliant on hardcoded linguistic rules, NLP's evolution significantly pivoted with the introduction of machine learning, particularly shallow learning algorithms, leading eventually to deep learning, which is the current standard.
Importance of Classical Methods: Knowing traditional methods is still valuable, providing a historical context and foundation for understanding NLP tasks. Traditional methods can be advantageous with small datasets or limited compute power.
Edit Distance and Stemming:
- Levenshtein Distance: Used for spelling corrections by measuring the minimal edits needed to transform one string into another.
- Stemming: Simplifying a word to its base form. The Porter Stemmer is a common algorithm used.
Language Models:
- Understand language legitimacy by calculating the joint probability of word sequences.
- Use n-grams for constructing language models to increase accuracy at the expense of computational power.
Naive Bayes for Classification:
- Ideal for tasks like spam detection, document classification, and sentiment analysis.
- Relies on a 'bag of words' model, simplifying documents down to word frequency counts and disregarding sequence dependence.
Part of Speech Tagging and Named Entity Recognition:
- Methods: Maximum entropy models, hidden Markov models.
- Challenges: Feature engineering for parts of speech, complexity in named entity recognition.
Generative vs. Discriminative Models:
- Generative Models: Estimate the joint probability distribution; useful with less data.
- Discriminative Models: Focus on decision boundaries between classes.
Topic Modeling with LDA:
- Latent Dirichlet Allocation (LDA) helps identify topics within large sets of documents by clustering words into topics, allowing for mixed membership of topics across documents.
Search and Similarity Measures:
- Utilize TF-IDF for transforming documents into vectors reflecting term importance inversely correlated with document frequency in the corpus.
- Employ cosine similarity for measuring semantic similarity between document vectors.

57 episodes

#Artificial #Intelligence #Introduction #Artificial Intelligence #Tech #Higher Education #OCDevel #Podcasting Education #Machining #Learn

MLG 019 Natural Language Processing 2

Machine Learning Guide

591 subscribers

published 8y ago

MP3•Episode home

Try a walking desk to stay healthy while you study or work!

Notes and resources at ocdevel.com/mlg/19

Classical NLP Techniques:

Origins and Phases in NLP History: Initially reliant on hardcoded linguistic rules, NLP's evolution significantly pivoted with the introduction of machine learning, particularly shallow learning algorithms, leading eventually to deep learning, which is the current standard.
Importance of Classical Methods: Knowing traditional methods is still valuable, providing a historical context and foundation for understanding NLP tasks. Traditional methods can be advantageous with small datasets or limited compute power.
Edit Distance and Stemming:
- Levenshtein Distance: Used for spelling corrections by measuring the minimal edits needed to transform one string into another.
- Stemming: Simplifying a word to its base form. The Porter Stemmer is a common algorithm used.
Language Models:
- Understand language legitimacy by calculating the joint probability of word sequences.
- Use n-grams for constructing language models to increase accuracy at the expense of computational power.
Naive Bayes for Classification:
- Ideal for tasks like spam detection, document classification, and sentiment analysis.
- Relies on a 'bag of words' model, simplifying documents down to word frequency counts and disregarding sequence dependence.
Part of Speech Tagging and Named Entity Recognition:
- Methods: Maximum entropy models, hidden Markov models.
- Challenges: Feature engineering for parts of speech, complexity in named entity recognition.
Generative vs. Discriminative Models:
- Generative Models: Estimate the joint probability distribution; useful with less data.
- Discriminative Models: Focus on decision boundaries between classes.
Topic Modeling with LDA:
- Latent Dirichlet Allocation (LDA) helps identify topics within large sets of documents by clustering words into topics, allowing for mixed membership of topics across documents.
Search and Similarity Measures:
- Utilize TF-IDF for transforming documents into vectors reflecting term importance inversely correlated with document frequency in the corpus.
- Employ cosine similarity for measuring semantic similarity between document vectors.

57 episodes

#Artificial #Intelligence #Introduction #Artificial Intelligence #Tech #Higher Education #OCDevel #Podcasting Education #Machining #Learn

All episodes

Welcome to Player FM!

Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.

Listen to 500+ topics

Similar to Machine Learning Guide

Podcasts Worth a Listen

Machine Learning Guide « » MLG 019 Natural Language Processing 2

MLG 019 Natural Language Processing 2

Podcasts Worth a Listen

Welcome to Player FM!

Similar to Machine Learning Guide

Machine Learning Guide « »
MLG 019 Natural Language Processing 2