nervaluate 0.1.7

NER evaluation done right

nervaluate

nervaluate is a python module for evaluating Named Entity Recognition (NER) models as defined in the SemEval 2013 - 9.1 task.

The evaluation metrics output by nervaluate go beyond a simple token/tag based schema, and consider diferent scenarios based on wether all the tokens that belong to a named entity were classified or not, and also wether the correct entity type was assigned.

This problem is described in detail in the original blog post by David Batista, and extends the code in the original repository which accompanied the blog post.

The code draws heavily on:

• Segura-bedmar, I., & Mart, P. (2013). 2013 SemEval-2013 Task 9 Extraction of Drug-Drug Interactions from. Semeval, 2(DDIExtraction), 341–350. link
• https://www.cs.york.ac.uk/semeval-2013/task9/data/uploads/semeval_2013-task-9_1-evaluation-metrics.pdf

Notes:

In scenarios IV and VI the entity type of the true and pred does not match, in both cases we only scored against the true entity, not the predicted one. You can argue that the predicted entity could also be scored as spurious, but according to the definition of spurius:

• Spurius (SPU) : system produces a response which doesn’t exist in the golden annotation;

In this case there exists an annotation, but with a different entity type, so we assume it's only incorrect.

Installation

To install the package:

pip install nervaluate

To create a virtual environment for development:

make virtualenv

# Then to activate the virtualenv:

source /build/virtualenv/bin/activate

Alternatively you can use your own virtualenv manager and simply make reqs to install requirements.

To run tests:

# Will run tox

make test

Example:

The main Evaluator class will accept a number of formats:

• prodi.gy style lists of spans.
• Nested lists containing NER labels.
• CoNLL style tab delimited strings.

Prodigy spans

true = [
    [{"label": "PER", "start": 2, "end": 4}],
    [{"label": "LOC", "start": 1, "end": 2},
     {"label": "LOC", "start": 3, "end": 4}]
]

pred = [
    [{"label": "PER", "start": 2, "end": 4}],
    [{"label": "LOC", "start": 1, "end": 2},
     {"label": "LOC", "start": 3, "end": 4}]
]

from nervaluate import Evaluator

evaluator = Evaluator(true, pred, tags=['LOC', 'PER'])

# Returns overall metrics and metrics for each tag

results, results_per_tag = evaluator.evaluate()

print(results)

{
    'ent_type':{
        'correct':3,
        'incorrect':0,
        'partial':0,
        'missed':0,
        'spurious':0,
        'possible':3,
        'actual':3,
        'precision':1.0,
        'recall':1.0
    },
    'partial':{
        'correct':3,
        'incorrect':0,
        'partial':0,
        'missed':0,
        'spurious':0,
        'possible':3,
        'actual':3,
        'precision':1.0,
        'recall':1.0
    },
    'strict':{
        'correct':3,
        'incorrect':0,
        'partial':0,
        'missed':0,
        'spurious':0,
        'possible':3,
        'actual':3,
        'precision':1.0,
        'recall':1.0
    },
    'exact':{
        'correct':3,
        'incorrect':0,
        'partial':0,
        'missed':0,
        'spurious':0,
        'possible':3,
        'actual':3,
        'precision':1.0,
        'recall':1.0
    }
}

print(results_by_tag)

{
    'LOC':{
        'ent_type':{
            'correct':2,
            'incorrect':0,
            'partial':0,
            'missed':0,
            'spurious':0,
            'possible':2,
            'actual':2,
            'precision':1.0,
            'recall':1.0
        },
        'partial':{
            'correct':2,
            'incorrect':0,
            'partial':0,
            'missed':0,
            'spurious':0,
            'possible':2,
            'actual':2,
            'precision':1.0,
            'recall':1.0
        },
        'strict':{
            'correct':2,
            'incorrect':0,
            'partial':0,
            'missed':0,
            'spurious':0,
            'possible':2,
            'actual':2,
            'precision':1.0,
            'recall':1.0
        },
        'exact':{
            'correct':2,
            'incorrect':0,
            'partial':0,
            'missed':0,
            'spurious':0,
            'possible':2,
            'actual':2,
            'precision':1.0,
            'recall':1.0
        }
    },
    'PER':{
        'ent_type':{
            'correct':1,
            'incorrect':0,
            'partial':0,
            'missed':0,
            'spurious':0,
            'possible':1,
            'actual':1,
            'precision':1.0,
            'recall':1.0
        },
        'partial':{
            'correct':1,
            'incorrect':0,
            'partial':0,
            'missed':0,
            'spurious':0,
            'possible':1,
            'actual':1,
            'precision':1.0,
            'recall':1.0
        },
        'strict':{
            'correct':1,
            'incorrect':0,
            'partial':0,
            'missed':0,
            'spurious':0,
            'possible':1,
            'actual':1,
            'precision':1.0,
            'recall':1.0
        },
        'exact':{
            'correct':1,
            'incorrect':0,
            'partial':0,
            'missed':0,
            'spurious':0,
            'possible':1,
            'actual':1,
            'precision':1.0,
            'recall':1.0
        }
    }
}

Nested lists

true = [
    ['O', 'O', 'B-PER', 'I-PER', 'O'],
    ['O', 'B-LOC', 'I-LOC', 'B-LOC', 'I-LOC', 'O'],
]

pred = [
    ['O', 'O', 'B-PER', 'I-PER', 'O'],
    ['O', 'B-LOC', 'I-LOC', 'B-LOC', 'I-LOC', 'O'],
]

evaluator = Evaluator(true, pred, tags=['LOC', 'PER'], loader="list")

results, results_by_tag = evaluator.evaluate()

CoNLL style tab delimited

true = "word\tO\nword\tO\B-PER\nword\tI-PER\n"

pred = "word\tO\nword\tO\B-PER\nword\tI-PER\n"

evaluator = Evaluator(true, pred, tags=['PER'], loader="conll")

results, results_by_tag = evaluator.evaluate()

Extending the package to accept more formats

Additional formats can easily be added to the module by creating a converstion function in nervaluate/utils.py, for example conll_to_spans(). This function must return the spans in the prodigy style dicts shown in the prodigy example above.

The new function can then be added to the list of loaders in nervaluate/nervaluate.py, and can then be selection with the loader argument when instantiating the Evaluator class.

A list of formats we intend to include is included in https://github.com/ivyleavedtoadflax/nervaluate/issues/3.