Stanford Temporal Tagger
Library for recognizing and normalizing time expressions in text.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
UnverifiedOverview
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
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
The library primarily supports English and German, which restricts its utility for applications requiring time extraction in other languages.
SUTime is designed to work within the Stanford CoreNLP framework, making it less flexible for integration into projects that do not use this framework.
Processing large volumes of text with SUTime can be computationally expensive and time-consuming due to its reliance on the CoreNLP pipeline.
The documentation for SUTime lacks detailed examples and explanations, making it difficult for new users to understand how to effectively use the library.
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
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Next step
Get Started with Stanford Temporal Tagger
Step-by-step setup guide with code examples and common gotchas.