NLP Processing

Process and analyze natural language using modern techniques and transformer models.

Overview

Natural Language Processing enables computers to understand, interpret, and generate human language. This skill covers fundamental techniques through state-of-the-art transformer models.

Quick Reference

Core Principles

1. Tokenization - Split text into meaningful units
2. Representation - Convert text to numerical vectors
3. Context - Capture word meaning from surrounding words
4. Transfer Learning - Leverage pre-trained models
5. Fine-Tuning - Adapt models to specific tasks

NLP Pipeline

1. Preprocessing

• Lowercasing, removing special characters
• Tokenization (word, subword, character)
• Stopword removal (task-dependent)
• Lemmatization or stemming

2. Representation

• Word embeddings (Word2Vec, GloVe)
• Contextual embeddings (BERT, RoBERTa)
• Sentence embeddings (Sentence-BERT)

3. Modeling

• Classical: Naive Bayes, SVM, Random Forest
• Deep Learning: RNNs, LSTMs, Transformers
• Pre-trained: BERT, GPT, T5

4. Post-processing

• Decoding, formatting
• Confidence scoring
• Error handling

Key Tasks

Text Classification:

• Sentiment analysis
• Topic classification
• Intent detection
• Spam detection

Sequence Labeling:

• Named Entity Recognition (NER)
• Part-of-Speech (POS) tagging
• Chunking

Text Generation:

• Summarization
• Translation
• Question answering
• Dialogue systems

Using the Reference Files

/references/preprocessing.md — Text cleaning, tokenization methods (word, subword, BPE), normalization, and handling special cases.

/references/classification.md — Text classification approaches, sentiment analysis, fine-tuning BERT/RoBERTa, evaluation metrics.

/references/sequence-labeling.md — NER, POS tagging, chunking, CRF layers, and sequence labeling with transformers.

/references/embeddings.md — Word2Vec, GloVe, BERT embeddings, Sentence-BERT, semantic similarity, and embedding fine-tuning.

Best Practices

• Use pre-trained transformers when possible
• Fine-tune on domain-specific data
• Handle class imbalance appropriately
• Use appropriate tokenization for your model
• Validate on diverse test sets
• Monitor for bias in predictions
• Consider computational constraints
• Document preprocessing steps

Common Pitfalls to Avoid

• Over-preprocessing (removing useful information)
• Not handling out-of-vocabulary words
• Ignoring class imbalance
• Using wrong tokenizer for model
• Not validating on diverse data
• Overfitting to small datasets
• Ignoring computational costs
• Not considering model bias