semantic-search-enrichment

What problem does it solve?

Semantic search enrichment fixes missed matches caused by morphological variation and semantic distance, where a literal substring or regex query fails to find conceptually related terms.

Core Features & Use Cases

• Linguistic normalization: lemmatizes inflected forms so “configured” matches “configure”.
• Synset-based semantic expansion: adds WordNet synonyms to capture near-meaning variants like “bug”→“defect” and “issue”→“problem”.
• Hypernym-chain expansion: optionally expands queries to broader concepts (hypernyms) to improve recall during audits and cross-artifact discovery.

Use cases: repository-wide code audits, configuration/wording change detection, and retrieval pipelines where recall matters more than precision.

Quick Start

Use semantic-search-enrichment to expand the term you are searching for, then run the resulting regex pattern against your codebase or document corpus.