Stanford Temporal Tagger
Library for recognizing and normalizing time expressions in text.
Pricing
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Adoption
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Open Source
Data freshness
—Overview
What is Stanford Temporal Tagger?
SUTime is a library that recognizes and normalizes time expressions within text, making it easier to process temporal data. It's particularly useful for applications requiring precise time extraction from unstructured text.
Key differentiator
“SUTime stands out as a specialized tool for time expression recognition, offering robust integration within the Stanford CoreNLP ecosystem.”
Capability profile
Strength Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
Fit analysis
Who is it for?
✓ Best for
Projects requiring precise extraction and normalization of time expressions from unstructured text.
Applications that process historical data with varying date formats.
✕ Not a fit for
Real-time processing applications where performance is critical.
Scenarios requiring support for languages other than English and German.
Cost structure
Pricing
Free Tier
None
Starts at
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Model
Flat rate
Enterprise
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Performance benchmarks
How Fast Is It?
Next step
Get Started with Stanford Temporal Tagger
Step-by-step setup guide with code examples and common gotchas.