Natural Language Processing Techniques

Why This Matters

In reproducible and collaborative data science, you're constantly working with text—documentation, code comments, research papers, survey responses, and communication logs. NLP techniques transform this unstructured text into analyzable, structured data that your entire team can work with programmatically. You're being tested on understanding not just what these techniques do, but how they fit into reproducible pipelines and why choosing the right technique matters for your analysis goals.

These methods fall into distinct categories: preprocessing and representation, structural analysis, semantic understanding, and generative applications. Don't just memorize definitions—know which technique solves which problem, how they build on each other, and when to apply them in a collaborative workflow. Understanding the computational tradeoffs and reproducibility considerations for each technique will serve you far better than surface-level recall.

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Text Preprocessing and Representation

Before any analysis can happen, raw text must be transformed into a format algorithms can process. These foundational techniques convert messy, variable-length strings into clean, numerical representations—the bridge between human language and machine computation.

Tokenization

• Breaks text into discrete units (tokens)—words, subwords, or characters depending on your tokenizer choice and downstream task requirements
• Critical first step in any NLP pipeline—inconsistent tokenization across team members destroys reproducibility; always document your tokenizer version and parameters
• Handles edge cases systematically—contractions, hyphenated words, and punctuation rules must be explicitly defined and shared in your preprocessing scripts

Word Embeddings (Word2Vec, GloVe)

• Maps words to dense numerical vectors—typically 100-300 dimensions, where similar words cluster together in vector space
• Captures semantic relationships mathematically—the classic example: $\vec{king} - \vec{man} + \vec{woman} \approx \vec{queen}$
• Enables transfer learning across projects—pre-trained embeddings (trained on billions of words) can be version-controlled and shared, ensuring all collaborators use identical representations

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Structural and Grammatical Analysis

Understanding how language is structured—not just what words appear—enables deeper analysis. These techniques extract grammatical patterns and relationships, revealing the skeleton beneath the surface of text.

Part-of-Speech (POS) Tagging

• Labels each token with grammatical role—nouns, verbs, adjectives, etc., using standardized tag sets like Penn Treebank for consistency across projects
• Provides syntactic context for downstream tasks—knowing "bank" is a noun (financial institution) vs. verb (to bank a turn) changes everything
• Reproducibility depends on model choice—different POS taggers (spaCy, NLTK, Stanford) produce slightly different results; document your choice explicitly

Named Entity Recognition (NER)

• Identifies and classifies proper nouns—people, organizations, locations, dates, and monetary values tagged with standardized labels
• Essential for information extraction pipelines—transforms unstructured text into structured database entries your team can query
• Model-dependent and domain-sensitive—a NER model trained on news articles may fail on scientific papers; always validate on your specific corpus

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Semantic Understanding and Classification

Moving beyond structure, these techniques interpret meaning—what the text is about, how the author feels, and what category it belongs to. This is where NLP starts answering real research questions.

Sentiment Analysis

• Quantifies emotional tone in text—outputs typically include polarity (positive/negative/neutral) and intensity scores between $-1$ and $+1$
• Enables large-scale opinion mining—analyze thousands of survey responses or social media posts programmatically rather than manually coding
• Requires careful validation for reproducibility—sentiment lexicons and models embed cultural assumptions; document your tool choice and test on domain-specific examples

Text Classification

• Assigns predefined labels to documents—spam detection, topic categorization, or any custom taxonomy your research requires
• Relies on labeled training data—your classification is only as good as your training set; version-control both data and model for reproducibility
• Supervised learning foundation—common algorithms include Naive Bayes, SVM, and neural classifiers; hyperparameters must be documented and shared

Topic Modeling

• Discovers latent themes across document collections—unsupervised technique that finds patterns without predefined categories
• LDA (Latent Dirichlet Allocation) is the classic algorithm—assumes each document is a mixture of topics, each topic a distribution over words
• Number of topics ($k$) is a key parameter—different $k$ values produce different results; document your choice and rationale for reproducibility

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Generative and Sequence-Based Applications

These advanced techniques don't just analyze text—they produce it. Language models predict sequences, summarizers condense information, and translators convert between languages. The computational complexity increases, but so does the capability.

Language Models (N-grams and Neural Models)

• Predict probability of word sequences—N-grams use fixed context windows (e.g., $P(w_n | w_{n-1}, w_{n-2})$ for trigrams), while neural models capture longer dependencies
• Foundation for text generation and completion—autocomplete, chatbots, and code suggestion tools all rely on language modeling
• Neural models require significant compute resources—reproducibility demands documenting model architecture, training data, random seeds, and hardware specifications

Text Summarization

• Condenses documents while preserving key information—extractive methods select existing sentences; abstractive methods generate new text
• Extractive is more reproducible—sentence selection algorithms produce consistent outputs; abstractive models may vary between runs
• Evaluation metrics matter—ROUGE scores (comparing n-gram overlap with reference summaries) are standard; always report which metrics you used

Machine Translation

• Converts text between languages automatically—modern systems use encoder-decoder neural architectures with attention mechanisms
• Context and cultural nuance are challenging—the same source text can have multiple valid translations, complicating evaluation
• API versioning is critical for reproducibility—if using services like Google Translate, document the date and version; outputs change as models update

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Quick Reference Table

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Self-Check Questions

1. Which two techniques both assign labels to individual tokens, and how do their purposes differ?

2. You're building a reproducible pipeline to analyze customer feedback. Put these steps in order: sentiment analysis, tokenization, word embeddings, text classification. Which step is optional depending on your classifier choice?

3. Compare and contrast Topic Modeling and Text Classification—when would you choose each for a collaborative research project, and what does each require from your team in terms of preparation?

4. A teammate runs your NLP pipeline and gets different results. Which three techniques from this guide are most sensitive to version differences or parameter choices, and what should you document to prevent this?

5. If an FRQ asks you to design a pipeline for extracting organization names from 10,000 news articles and categorizing them by industry, which techniques would you chain together and in what order?