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
| Scenario |
Recommended Approach |
Reference File |
| Text preprocessing and tokenization |
Cleaning, normalization, tokenization |
/references/preprocessing.md |
| Text classification and sentiment analysis |
Fine-tuned transformers or classical ML |
/references/classification.md |
| Named entities, POS tagging, parsing |
Sequence labeling with transformers |
/references/sequence-labeling.md |
| Embeddings and semantic similarity |
Sentence transformers, BERT embeddings |
/references/embeddings.md |
Core Principles
- Tokenization - Split text into meaningful units
- Representation - Convert text to numerical vectors
- Context - Capture word meaning from surrounding words
- Transfer Learning - Leverage pre-trained models
- 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
1---2name: nlp-processing-23description: Process and analyze natural language using modern NLP techniques. Use for text classification, named entity recognition, sentiment analysis, tokenization, embeddings, transformers (BERT, GPT), and language understanding tasks.4---5
6# NLP Processing
7
8Process and analyze natural language using modern techniques and transformer models.
9
10## Overview
11
12Natural Language Processing enables computers to understand, interpret, and generate human language. This skill covers fundamental techniques through state-of-the-art transformer models.
13
14## Quick Reference
15
16| Scenario | Recommended Approach | Reference File |
17|----------|---------------------|----------------|
18| Text preprocessing and tokenization | Cleaning, normalization, tokenization | `/references/preprocessing.md` |
19| Text classification and sentiment analysis | Fine-tuned transformers or classical ML | `/references/classification.md` |
20| Named entities, POS tagging, parsing | Sequence labeling with transformers | `/references/sequence-labeling.md` |
21| Embeddings and semantic similarity | Sentence transformers, BERT embeddings | `/references/embeddings.md` |
22
23## Core Principles
24
251. **Tokenization** - Split text into meaningful units
262. **Representation** - Convert text to numerical vectors
273. **Context** - Capture word meaning from surrounding words
284. **Transfer Learning** - Leverage pre-trained models
295. **Fine-Tuning** - Adapt models to specific tasks
30
31## NLP Pipeline
32
33### 1. Preprocessing
34- Lowercasing, removing special characters
35- Tokenization (word, subword, character)
36- Stopword removal (task-dependent)
37- Lemmatization or stemming
38
39### 2. Representation
40- Word embeddings (Word2Vec, GloVe)
41- Contextual embeddings (BERT, RoBERTa)
42- Sentence embeddings (Sentence-BERT)
43
44### 3. Modeling
45- Classical: Naive Bayes, SVM, Random Forest
46- Deep Learning: RNNs, LSTMs, Transformers
47- Pre-trained: BERT, GPT, T5
48
49### 4. Post-processing
50- Decoding, formatting
51- Confidence scoring
52- Error handling
53
54## Key Tasks
55
56**Text Classification:**
57- Sentiment analysis
58- Topic classification
59- Intent detection
60- Spam detection
61
62**Sequence Labeling:**
63- Named Entity Recognition (NER)
64- Part-of-Speech (POS) tagging
65- Chunking
66
67**Text Generation:**
68- Summarization
69- Translation
70- Question answering
71- Dialogue systems
72
73## Using the Reference Files
74
75**`/references/preprocessing.md`** — Text cleaning, tokenization methods (word, subword, BPE), normalization, and handling special cases.
76
77**`/references/classification.md`** — Text classification approaches, sentiment analysis, fine-tuning BERT/RoBERTa, evaluation metrics.
78
79**`/references/sequence-labeling.md`** — NER, POS tagging, chunking, CRF layers, and sequence labeling with transformers.
80
81**`/references/embeddings.md`** — Word2Vec, GloVe, BERT embeddings, Sentence-BERT, semantic similarity, and embedding fine-tuning.
82
83## Best Practices
84
85- Use pre-trained transformers when possible
86- Fine-tune on domain-specific data
87- Handle class imbalance appropriately
88- Use appropriate tokenization for your model
89- Validate on diverse test sets
90- Monitor for bias in predictions
91- Consider computational constraints
92- Document preprocessing steps
93
94## Common Pitfalls to Avoid
95
96- Over-preprocessing (removing useful information)
97- Not handling out-of-vocabulary words
98- Ignoring class imbalance
99- Using wrong tokenizer for model
100- Not validating on diverse data
101- Overfitting to small datasets
102- Ignoring computational costs
103- Not considering model bias
104
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