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FAQs
- Which model type (i.e. "basic", "standard" and "full") should I use?
- How fast is a basic/standard/full model? Can I tune the speed?
A: The short answer is "full" for best accuracy, "standard" for a good accuracy-speed trade-off, and "basic" for best speed.
The basic model type uses 1st-order features, and runs Chu-Liu-Edmond algorithm for decoding. The standard model type involves up to 3rd-order features and approximation decoding. The full model type adds two additional types of 3rd-order features, and some global features from re-ranking literature. Therefore it is the most accurate but the slowest model type.
A: Actual parsing speed varies depending on the sentence length and the size of the model (i.e. number of parameters). Typically, a basic model is about 2x~3x faster than a standard one, and the latter is about 2x faster than a full model.
Here are some options to obtain better parsing speed:
- use option "label:false" if dependency label is not required
- use more threads in parallel for decoding (e.g. "thread:6")
- for standard/full model type, change the decoding converge threshold to trade-off between speed and accuracy (e.g. "converge-test:k"; k=1 is the fastest and other values in [20, 300] are also reasonable)
- use the basic model type (i.e. "model:basic")