Spancat: a new approach for span labeling

2022-06-14 · Source: Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

The SpanCategorizer, or Spancat, is a new spaCy component designed to meet the NLP community's demand for structured annotation across diverse labeled spans. This includes handling long phrases, non-named entities, and overlapping annotations, which are common challenges in advanced natural language processing tasks. Introduced in a recent blog post, Spancat aims to simplify and enhance the process of creating detailed linguistic annotations. The component provides a flexible framework for users to define and categorize arbitrary text spans, moving beyond traditional named entity recognition to support more complex and nuanced data labeling requirements. This release highlights new features intended to assist with various span labeling needs, offering a robust solution for researchers and developers working with intricate textual data.

Key takeaway

For NLP engineers and data scientists working with complex text annotation, Spancat offers a crucial upgrade to spaCy's capabilities. If your current annotation workflows struggle with long phrases, non-named entities, or overlapping text spans, you should explore integrating this new component. It provides a robust framework to define and categorize arbitrary text segments, potentially streamlining your data labeling process and enabling more nuanced linguistic analysis. Consider evaluating Spancat's new features to enhance the precision and flexibility of your NLP projects.

Key insights

The SpanCategorizer (Spancat) is a spaCy component for structured annotation of diverse, complex, and overlapping text spans.

In practice

• Structured annotation for long phrases
• Labeling non-named entities
• Managing overlapping annotations

Topics

• Span Categorization
• spaCy Component
• NLP Annotation
• Named Entity Recognition
• Text Labeling
• Overlapping Spans

Best for: NLP Engineer, Machine Learning Engineer, Data Scientist

Related on AIssential

• Introducing Span Categorization in Prodigy and spaCy — Span Categorization (spancat) in spaCy extracts and classifies long, overlapping text spans, providing explicit control and confidence scores.
• SpanCat with spaCy and Prodigy on real data — Span classification with spaCy and Prodigy enables robust extraction of nested entities from messy OCR data, crucial for complex information retrieval.
• A Scalable Tool for Measuring Manner and Result Verbs in Developmental Language Research — A RoBERTa classifier, trained on LLM-generated annotations, accurately distinguishes manner and result verbs at scale.
• Explicit Evidence Grounding via Structured Inline Citation Generation — LLMs excel at document retrieval but struggle with precise evidence span identification for faithful inline citations.
• SRA: Span Representation Alignment for Large Language Model Distillation — Aligning tokenizer-agnostic spans via their attention-weighted Center of Mass improves cross-tokenizer knowledge distillation.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai.