MLG 019 Natural Language Processing 2

Jul 10, 2017
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Classical natural language processing (NLP) techniques involve a progression from rule-based linguistics approaches to machine learning, and eventually deep learning as state-of-the-art. Despite the prevalence of deep learning in modern NLP, understanding traditional methods like naive Bayes and hidden Markov models offers foundational insights and historical context, especially useful when dealing with smaller data sets or limited compute resources.

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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.