Universal Dependencies v2.5 Benchmarks for spaCy

2021-12-14 · Source: Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Intermediate, quick

Summary

Universal Dependencies v2.5 benchmarks for spaCy v3.2 have been released, showcasing the framework's competitive performance in natural language processing tasks, particularly in dependency parsing. These new benchmarks provide a direct comparison of spaCy v3.2 against other prominent NLP libraries, specifically Stanza and Trankit. The evaluation methodology employed for this comparison is the end-to-end evaluation from the CoNLL 2018 Shared Task, ensuring a standardized and rigorous assessment of parsing accuracy and efficiency. The results highlight spaCy's capability to deliver strong performance in dependency parsing and related tasks when measured against established tools in the field, affirming its utility for developers and researchers.

Key takeaway

For NLP engineers evaluating dependency parsing libraries, spaCy v3.2 presents a strong contender. Its demonstrated competitive performance against established tools like Stanza and Trankit on Universal Dependencies v2.5 benchmarks, using the rigorous CoNLL 2018 Shared Task evaluation, suggests it is a robust choice. You should consider integrating spaCy v3.2 into your projects requiring high-quality, efficient dependency parsing, especially if you prioritize performance validated by industry-standard benchmarks.

Key insights

spaCy v3.2 demonstrates competitive performance against Stanza and Trankit on Universal Dependencies v2.5 benchmarks using CoNLL 2018 evaluation.

Method

Performance was evaluated using the end-to-end evaluation from the CoNLL 2018 Shared Task, comparing spaCy v3.2 directly with Stanza and Trankit on Universal Dependencies v2.5 benchmarks.

Topics

• Universal Dependencies
• spaCy
• NLP Benchmarking
• Dependency Parsing
• CoNLL Shared Task
• Stanza Trankit Comparison

Best for: Research Scientist, NLP Engineer, Machine Learning Engineer, AI Scientist

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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.