Natural Language Processing
WordNet
A Short Primer, with Python Code Examples
What?
WordNet is a large lexical database (a database that contains information about words) of English. It groups English words into sets of synonyms called synsets. It provides short definitions and usage examples. It also records the various semantic relations between these synonym sets.
Why WordNet?
WordNet is widely used in natural language processing (NLP) and computational linguistics. It helps computers understand human language. WordNet’s structure makes it useful for various tasks, like word sense disambiguation and information retrieval.
The Structure of WordNet
WordNet has a unique structure. It organizes nouns, verbs, adjectives, and adverbs into synsets. Each synset represents a unique concept. Synsets are connected by several types of relations:
- Synonymy — Words that have similar meanings.
- Antonymy — Words with opposite meanings.
- Hyponymy — A more specific concept.
- Hypernymy — A more general concept
- Meronymy — Part-whole relationships.
- Holonymy— Whole-part relationships.
Practical Applications of WordNet
- Word Sense Disambiguation (WSD): Identifying which sense of a word is used in a sentence.
- Information Retrieval: Enhancing search engines to understand synonyms and related terms.
- Machine Translation: Improving the accuracy of translations by understanding context.
- Text Classification: Categorizing text based on content.
- Sentiment Analysis: Understanding the sentiment expressed in text.
Python Code Examples
To use WordNet in Python, we can use the Natural Language Toolkit (NLTK).
Installation
Basic Usage
Output
