nltk.metrics.agreement

Implementations of inter-annotator agreement coefficients surveyed by Artstein and Poesio (2007), Inter-Coder Agreement for Computational Linguistics.

An agreement coefficient calculates the amount that annotators agreed on label assignments beyond what is expected by chance.

In defining the AnnotationTask class, we use naming conventions similar to the paper's terminology. There are three types of objects in an annotation task:

the coders (variables "c" and "C") the items to be annotated (variables "i" and "I") the potential categories to be assigned (variables "k" and "K")

Additionally, it is often the case that we don't want to treat two different labels as complete disagreement, and so the AnnotationTask constructor can also take a distance metric as a final argument. Distance metrics are simply functions that take two arguments, and return a value between 0.0 and 1.0 indicating the distance between them. If not supplied, the default is binary comparison between the arguments.

The simplest way to initialize an AnnotationTask is with a list of triples, each containing a coder's assignment for one object in the task:

task = AnnotationTask(data=[('c1', '1', 'v1'),('c2', '1', 'v1'),...])

Note that the data list needs to contain the same number of triples for each individual coder, containing category values for the same set of items.

Alpha (Krippendorff 1980) Kappa (Cohen 1960) S (Bennet, Albert and Goldstein 1954) Pi (Scott 1955)

TODO: Describe handling of multiple coders and missing data

Expected results from the Artstein and Poesio survey paper:

>>> from nltk.metrics.agreement import AnnotationTask
>>> import os.path
>>> t = AnnotationTask(data=[x.split() for x in open(os.path.join(os.path.dirname(__file__), "artstein_poesio_example.txt"))])
>>> t.avg_Ao()
0.88
>>> t.pi()
0.7995322418977615...
>>> t.S()
0.8199999999999998...

This would have returned a wrong value (0.0) in @785fb79 as coders are in the wrong order. Subsequently, all values for pi(), S(), and kappa() would have been wrong as they are computed with avg_Ao(). >>> t2 = AnnotationTask(data=[('b','1','stat'),('a','1','stat')]) >>> t2.avg_Ao() 1.0

The following, of course, also works. >>> t3 = AnnotationTask(data=[('a','1','othr'),('b','1','othr')]) >>> t3.avg_Ao() 1.0