spacy-nlp-pipeline

What problem does it solve?

This Skill helps you turn raw text into structured linguistic signals (tokens, POS tags, dependency parses, and named entities) so you can analyze language at scale without manual annotation.

Core Features & Use Cases

• spaCy pipeline setup & model selection: Load and configure spaCy models (including multilingual choices) and control which components run for speed.
• Domain-specific NER with EntityRuler: Add custom patterns to recognize entities using rule-based matching, optionally before the statistical NER.
• Dependency-driven insights (SVO extraction) & visualization: Extract subject–verb–object triples from parsed text and visualize entities/dependencies with displacy for rapid qualitative validation.
• Batch processing for large corpora: Use nlp.pipe() to run NER efficiently over many documents and then aggregate entity frequencies.

Quick Start

Use the spacy-nlp-pipeline skill to build an EntityRuler-enhanced NER pipeline for a batch of texts and return extracted entities with character spans.