Precision and Recall of ldig (twitter language detection)

In the previous article, I introduced ldig (Language Detection with Infinity-Gram: site, blog), which detects tweet languages.
There are some requests to tell ldig’s precision and recall, so I calculated them.

lang	size	detected	correct	precision	recall
cs	5329	5330	5319	0.9979	0.9981
da	5478	5483	5311	0.9686	0.9695
de	10065	10076	10014	0.9938	0.9949
en	9701	9670	9569	0.9896	0.9864
es	10066	10075	9989	0.9915	0.9924
fi	4490	4472	4459	0.9971	0.9931
fr	10098	10097	10048	0.9951	0.9950
id	10181	10233	10167	0.9936	0.9986
it	10150	10191	10109	0.9920	0.9960
nl	9671	9579	9521	0.9939	0.9845
no	8560	8442	8219	0.9736	0.9602
pl	10070	10079	10054	0.9975	0.9984
pt	9422	9441	9354	0.9908	0.9928
ro	5914	5831	5822	0.9985	0.9844
sv	9990	10034	9866	0.9833	0.9876
tr	10310	10321	10300	0.9980	0.9990
vi	10494	10486	10479	0.9993	0.9986
total	149989		148600		0.9907

The sum of data size is not equal to the amount of detected languages because ldig outputs “” as language when the max probability is lower than 0.6.
And the data size is not equal to one in the previous article because the dataset is updated.

I reckoned it doesn’t make sense over 99% accuracy, then what’s about?

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