Natural Language Processing (NLP) is a field at the intersection of computer science, linguistics, and artificial intelligence that focuses on enabling machines to understand, interpret, and generate human language.
NLP powers applications like chatbots, search engines, language translation, voice assistants, and text analytics.
Core Goals of NLP
- Understanding language: Parsing and extracting meaning from text or speech.
- Generating language: Producing human-like text or speech.
- Interpreting context: Recognizing tone, sentiment, and intent.
- Structuring unstructured data: Turning natural language into structured formats for computation.
Key Components
| Component | Description |
|---|---|
| Tokenization | Breaking text into words, sentences, or symbols. |
| Part-of-Speech Tagging | Identifying grammatical roles (noun, verb, etc.). |
| Named Entity Recognition (NER) | Extracting proper names (people, places, dates, etc.). |
| Parsing | Analyzing grammatical structure (e.g. syntax trees). |
| Lemmatization/Stemming | Reducing words to their root or base forms. |
| Sentiment Analysis | Detecting emotional tone in text. |
| Text Classification | Assigning categories (e.g. spam vs. not spam). |
| Machine Translation | Translating text from one language to another. |
| Text Generation | Producing coherent, human-like text. |
Approaches to NLP
Rule-Based Systems
- Use handcrafted linguistic rules.
- Effective for narrow domains but limited scalability.
Statistical NLP
- Based on probability and statistics (e.g. Naive Bayes, HMMs).
- Relies on annotated datasets and corpus frequency.
Machine Learning-Based NLP
- Uses algorithms to learn patterns from data (e.g. SVM, decision trees).
- Requires feature engineering.
Deep Learning and Transformers
- Uses neural networks (especially LSTMs, GRUs, Transformers).
- Transformers like BERT, GPT, and T5 dominate modern NLP tasks.
- Enables zero-shot and few-shot learning.
Popular NLP Libraries and Tools
| Library | Language | Use |
|---|---|---|
| spaCy | Python | Fast and efficient NLP pipeline |
| NLTK | Python | Academic, linguistic-focused |
| Transformers (Hugging Face) | Python | Pre-trained models and training |
| Stanza | Python | Stanford NLP toolkit |
| OpenNLP | Java | Tokenization, NER, parsing |
| CoreNLP | Java | Deep linguistic analysis |
Challenges in NLP
- Ambiguity: Words and sentences can have multiple meanings.
- Context dependence: Meaning can vary based on surrounding text.
- Low-resource languages: Lack of data for many world languages.
- Bias and fairness: NLP systems can reflect societal biases.
- Scalability: Processing large-scale corpora efficiently.