Features
Features:

Product Tour >

Edraw AI >

Paid Plans:

Individuals >

Business >

Eduaction >
Resources
Blog

History

How-tos & Tips

Discovery

Biography

Business Analysis

Examples

AI concept Map

Free AI Mind Map Generator

Onenote Mind Map

Bcg Matrix Examples

Nike Marketing Strategy

Unilever SWOT Analysis

Make Mind Maps in Google Docs

Guide

FAQs

What's New

Resource Center
Templates
All Templates

Brain Storming Templates

Strategy and Planning Templates

Project Management Templates

Product Management Templates

Human Resources Templates

Agile Workflow Templates

Marketing Templates

Education Templates

Fun and Games Templates

User Gallery
Download
Pricing
Enterprise

MindMap Gallery 自然语言处理基础导图

自然语言处理基础导图

Unlock the power of language with our comprehensive exploration of Natural Language Processing (NLP) fundamentals! This guide covers essential topics including the definition of NLP and its relationships to linguistics and machine learning. Dive into core NLP tasks such as text classification, sequence labeling, information extraction, and parsing. Discover data foundations with insights on data sources, preparation, and challenges. Learn about text representation through classic and distributed methods, and explore various modeling approaches including rule-based and symbolic methods. Join us on this journey to understand how computers can comprehend and generate human language effectively!

Edited at 2026-03-20 03:56:57

WSNG3jTL

Recent works View more works>>

自然语言处理基础导图

WSNG3jTL

Recent works View more works>>

Recommended to you
Outline

Key deep learning models
- 41
- 1
Mason·Carter

Natural Language Processing (NLP) Fundamentals Mind Map

1. What is NLP

Definition

Enables computers to understand, interpret, generate, and interact using human language (text/speech)

Relationship to other fields

Linguistics (syntax, semantics, pragmatics)

Machine Learning / Deep Learning

Information Retrieval (search, ranking)

Speech Processing (ASR/TTS)

Typical NLP pipeline (high-level)

Data collection → Cleaning → Annotation → Modeling → Evaluation → Deployment → Monitoring

2. Core NLP Tasks

2.1 Text Classification

Sentiment analysis (polarity; aspect-based)

Topic classification / intent detection

Toxicity / spam / hate speech detection

Document categorization (news, legal, medical)

2.2 Sequence Labeling

Named Entity Recognition (NER)

Entity types: person, organization, location, time, product, medical codes, etc.

Part-of-Speech (POS) tagging

Chunking (shallow parsing)

Slot filling (in dialogue systems)

2.3 Information Extraction (IE)

Relation extraction (e.g., works_for(Person, Company))

Event extraction (who did what, when, where)

Entity linking (mentions → knowledge base entities)

Knowledge graph population

2.4 Parsing & Linguistic Analysis

Dependency parsing (head-dependent relations)

Constituency parsing (phrase structure)

Coreference resolution (he/she/it → entity)

Word sense disambiguation (bank: river vs finance)

2.5 Information Retrieval & Search

Document retrieval (BM25, dense retrieval)

Query understanding and expansion

Passage ranking / reranking

Question answering retrieval component (RAG)

2.6 Machine Translation

Neural machine translation (NMT)

Multilingual translation

Domain adaptation (general → legal/medical)

2.7 Summarization

Extractive summarization (select sentences)

Abstractive summarization (generate new text)

Long-document summarization; meeting summarization

2.8 Question Answering (QA)

Extractive QA (answer span in context)

Generative QA (free-form answers)

Open-domain QA (requires retrieval)

Conversational QA (multi-turn context)

2.9 Text Generation & Dialogue

Chatbots / assistants

Controlled generation (style, safety, constraints)

Story generation, paraphrasing, rewriting

2.10 Semantic Similarity & Matching

Text similarity (STS), duplicate detection

Semantic search embeddings

Entailment / Natural Language Inference (NLI)

2.11 Speech-Related (often adjacent to NLP)

Automatic Speech Recognition (ASR)

Text-to-Speech (TTS)

Spoken language understanding (SLU)

3. Data Foundations

3.1 Data Sources

Web text, books, news, social media

Domain text (clinical notes, legal contracts, product reviews)

Conversational logs (customer support)

Multilingual corpora

3.2 Data Preparation

Cleaning (HTML removal, deduplication, normalization)

Tokenization decisions (word vs subword vs character)

Handling casing, punctuation, emojis, hashtags

Dealing with noisy text (typos, slang)

Train/validation/test splits (avoid leakage)

3.3 Annotation & Labeling

Label schemas (BIO tagging for NER)

Annotation guidelines and inter-annotator agreement

Active learning for efficient labeling

Weak supervision / distant supervision

Synthetic data generation (with caution)

3.4 Data Challenges

Class imbalance

Domain shift

Multilingual / code-switching

Privacy and sensitive data (PII)

Bias and representativeness

4. Text Representation (Features)

4.1 Classic Representations

Bag of Words (BoW)

TF-IDF

N-grams (word/character n-grams)

Sparse vectors; interpretability

4.2 Distributed Representations (Embeddings)

Word embeddings

Word2Vec (CBOW, Skip-gram)

GloVe

FastText (subword information)

Sentence/document embeddings

Sentence-BERT, Universal Sentence Encoder

Contextual embeddings

ELMo, BERT-style token representations

4.3 Subword Tokenization

BPE, WordPiece, Unigram LM

Benefits: open vocabulary, multilingual handling

Tradeoffs: token fragmentation, length inflation

4.4 Feature Engineering (still useful)

Lexicons (sentiment dictionaries)

Morphological features (prefix/suffix)

Metadata signals (author, timestamp)

5. Modeling Approaches

5.1 Rule-Based & Symbolic Methods

Regular expressions, pattern matching

Grammars and finite-state machines

Advantages: control, interpretability

Limitations: brittleness, scalability

5.2 Classical Machine Learning

Naive Bayes, Logistic Regression

SVMs, Random Forests

CRFs for sequence labeling

Typical workflow: feature extraction → model training → evaluation

5.3 Neural Networks (Pre-Transformer)

Feed-forward networks for classification

CNNs for text (local patterns)

RNNs/LSTMs/GRUs for sequences

Seq2Seq with attention (translation, summarization)

5.4 Transformers & Foundation Models

Transformer basics

Self-attention, positional encoding

Encoder, decoder, encoder-decoder

Popular model families

Encoder-only (BERT, RoBERTa) for understanding tasks

Decoder-only (GPT-style) for generation

Encoder-decoder (T5, BART) for translation/summarization

Training paradigms

Pretraining (masked LM / causal LM)

Fine-tuning (task-specific)

Instruction tuning and alignment

Parameter-efficient adaptation

Adapters, LoRA, prefix/prompt tuning

Retrieval-Augmented Generation (RAG)

Retriever + generator; grounding in documents

Multimodal extensions (text+image; broader than basic NLP)

5.5 Prompting & In-Context Learning

Zero-shot / few-shot prompting

Prompt templates and guardrails

Structured outputs (JSON schemas)

Limitations: hallucination, sensitivity to wording

Modeling progressed from rules → classic ML → neural sequence models → transformers/foundation models, with prompting and RAG as system-level techniques around LLMs.

6. Training, Optimization, and Evaluation

6.1 Training Concepts

Loss functions

Cross-entropy for classification

Token-level negative log-likelihood for LM

Optimization

SGD, Adam/AdamW

Learning rate schedules, warmup

Regularization

Dropout, weight decay, early stopping

Handling long texts

Truncation, sliding window, chunking, long-context models

6.2 Evaluation Metrics (by task)

Classification

Accuracy, Precision, Recall, F1

ROC-AUC / PR-AUC (imbalanced)

Sequence labeling (NER/POS)

Token-level vs entity-level F1 (strict match)

Machine translation

BLEU, chrF, COMET

Summarization

ROUGE, BERTScore

Faithfulness/hallucination checks

Language modeling / generation

Perplexity (limited proxy)

Human evaluation (helpfulness, correctness, style)

Retrieval

Recall@k, MRR, nDCG

Exact Match (EM), F1, accuracy

6.3 Error Analysis

Confusion matrix; per-class breakdown

Slice analysis (by length, domain, demographic group)

Calibration and confidence

Robustness checks (typos, paraphrases, adversarial)

6.4 Benchmarking & Reproducibility

Fixed seeds; dataset versions

Baselines and ablations

Model cards and experiment tracking

7. Common NLP Techniques (Practical Toolkit)

7.1 Preprocessing

Sentence segmentation

Tokenization and normalization

Stopword removal (task-dependent)

Stemming vs lemmatization

7.2 Vectorization & Similarity

Cosine similarity

Approximate nearest neighbors (ANN) for embeddings

Clustering (k-means, hierarchical) for topic discovery

7.3 Topic Modeling

LDA (probabilistic topics)

Neural topic models; embedding-based topics (BERTopic)

7.4 Text Generation Control

Decoding methods

Greedy, beam search

Top-k, nucleus (top-p), temperature

Safety filters and constraints

Bad word lists, regex constraints

Constrained decoding (structured formats)

7.5 Retrieval-Augmented Systems

Document chunking strategies

Embedding selection (bi-encoders)

Reranking (cross-encoders)

Citations/attribution and grounding

8. Tools & Libraries

8.1 Core Python NLP Libraries

NLTK (education, classic NLP)

spaCy (production NLP pipelines)

Gensim (topic modeling, embeddings)

Stanza (neural pipelines)

8.2 Deep Learning Frameworks

PyTorch

TensorFlow / Keras

JAX (research/accelerated training)

8.3 Transformer Ecosystem

Hugging Face Transformers (models, tokenizers)

Hugging Face Datasets (data loading, benchmarks)

Hugging Face Tokenizers (fast tokenization)

Accelerate / DeepSpeed (distributed training)

PEFT libraries (LoRA/adapters)

8.4 Data & Annotation Tools

Label Studio (annotation)

Prodigy (annotation; spaCy ecosystem)

doccano (text classification/NER annotation)

8.5 Experiment Tracking & MLOps

Weights & Biases, MLflow

DVC (data versioning)

Docker (packaging), Kubernetes (deployment)

8.6 Retrieval & Vector Databases (for RAG)

FAISS (ANN search library)

Elasticsearch / OpenSearch (BM25 + vector)

Vector DBs: Pinecone, Weaviate, Milvus, Qdrant

8.7 Deployment

FastAPI (serving)

ONNX / TorchScript (optimization)

Quantization libraries (e.g., bitsandbytes)

9. Practical Applications

Customer support automation (chat, ticket triage)

Search and recommendation (semantic search)

Business intelligence (feedback mining, trend detection)

Healthcare (clinical coding, de-identification)

Legal (contract review, clause extraction)

Finance (news analysis, risk signals)

Education (grading assistance, tutoring)

Content moderation and safety

10. Key Challenges & Limitations

Ambiguity and context dependence

Polysemy, sarcasm, implicit meaning

Hallucination in generative models

Need grounding, verification, citations

Bias, fairness, and toxicity

Data-driven biases; mitigation and evaluation

Privacy and security

PII leakage, prompt injection (in LLM apps)

Domain generalization

Performance drops outside training distribution

Multilingual and low-resource languages

Data scarcity; transfer learning

Efficiency and cost

Latency, memory, energy; model compression

11. Ethics, Safety, and Responsible NLP

Transparency

Model cards, data statements

Fairness & bias mitigation

Balanced datasets, debiasing techniques, audits

Safety controls

Content filtering, refusal policies, red teaming

Privacy-preserving methods

De-identification, differential privacy (advanced)

Legal and compliance

12. Learning Path (Suggested Progression)

Foundations

Linguistics basics (syntax/semantics)

Python + linear algebra + probability

Classic NLP

Tokenization, n-grams, TF-IDF, Naive Bayes, CRFs

Neural NLP

Embeddings → RNNs/CNNs → attention

Transformers & LLMs

Fine-tuning, prompting, evaluation, RAG

Building systems

Data pipelines, monitoring, safety, deployment

Hands-on projects

Sentiment classifier → NER model → semantic search → RAG QA assistant