An overview of what Natural Language Processing (NLP) is, its applications, and its significance in the field of artificial intelligence and data science.
Introduction to Natural Language Processing (NLP)
Key concepts, goals, and foundational techniques
| Aspect | Description |
|---|---|
| Definition | A sub‑field of artificial intelligence that enables computers to understand, interpret, and generate human language. |
| Scope | Text & speech processing, sentiment analysis, machine translation, question answering, chatbots, information extraction, summarization, etc. |
| Why It Matters | Bridges the gap between human communication and digital data, powering applications from search engines to virtual assistants. |
| Goal | Typical Tasks | Example |
|---|---|---|
| Understanding | Parsing, part‑of‑speech tagging, named‑entity recognition (NER) | Identify “Apple” as a company in a news article. |
| Generation | Text summarization, machine translation, dialogue generation | Produce a concise summary of a research paper. |
| Interaction | Question answering, sentiment analysis, intent detection | Detect user intent in a chatbot message. |
| Knowledge Extraction | Relation extraction, event detection, knowledge‑base population | Pull out “CEO of” relationships from corporate filings. |
Data Collection
Pre‑processing
Feature Extraction
Modeling
Post‑processing & Evaluation
| Technique | Brief Explanation | Typical Use‑Cases |
|---|---|---|
| Regular Expressions | Pattern matching on character strings. | Simple entity extraction, data cleaning. |
| Part‑of‑Speech (POS) Tagging | Assign grammatical categories (noun, verb, etc.). | Syntactic parsing, feature creation. |
| Named‑Entity Recognition (NER) | Detect proper nouns (people, locations, organizations). | Information extraction, knowledge graphs. |
| Dependency & Constituency Parsing | Build tree structures showing grammatical relations. | Semantic role labeling, relation extraction. |
| Word Embeddings | Dense vector representations capturing semantic similarity. | Text classification, clustering, similarity search. |
| Transformers | Self‑attention mechanism; enables parallel processing of sequences. | State‑of‑the‑art language models (BERT, GPT). |
| Sequence‑to‑Sequence (Seq2Seq) | Encoder‑decoder models for mapping input sequences to output sequences. |