nervaluate 1.1.0

NER evaluation considering partial match scoring

         

nervaluate

nervaluate is a 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 different scenarios based on whether all the tokens that belong to a named entity were classified or not, and also whether the correct entity type was assigned.

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

The code draws heavily on the papers:

• SemEval-2013 Task 9 : Extraction of Drug-Drug Interactions from Biomedical Texts (DDIExtraction 2013)

• SemEval-2013 Task 9.1 - Evaluation Metrics

Usage example

pip install nervaluate

A possible input format are lists of NER labels, where each list corresponds to a sentence and each label is a token label. Initialize the Evaluator class with the true labels and predicted labels, and specify the entity types we want to evaluate.

from nervaluate.evaluator import Evaluator

true = [
    ['O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'B-ORG', 'I-ORG'],  # "The John Smith who works at Google Inc"
    ['O', 'B-LOC', 'B-PER', 'I-PER', 'O', 'O', 'B-DATE'],      # "In Paris Marie Curie lived in 1895"
]
  
pred = [
    ['O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-ORG', 'I-ORG'],
    ['O', 'B-LOC', 'I-LOC', 'B-PER', 'O', 'O', 'B-DATE'],
]
   
evaluator = Evaluator(true, pred, tags=['PER', 'ORG', 'LOC', 'DATE'], loader="list")

Print the summary report for the evaluation, which will show the metrics for each entity type and evaluation scenario:

print(evaluator.summary_report())

Scenario: all

              correct   incorrect     partial      missed    spurious   precision      recall    f1-score

ent_type            5           0           0           0           0        1.00        1.00        1.00
   exact            2           3           0           0           0        0.40        0.40        0.40
 partial            2           0           3           0           0        0.40        0.40        0.40
  strict            2           3           0           0           0        0.40        0.40        0.40

or aggregated by entity type under a specific evaluation scenario:

print(evaluator.summary_report(mode='entities'))  
  
Scenario: strict

             correct   incorrect     partial      missed    spurious   precision      recall    f1-score

   DATE            1           0           0           0           0        1.00        1.00        1.00
    LOC            0           1           0           0           0        0.00        0.00        0.00
    ORG            1           0           0           0           0        1.00        1.00        1.00
    PER            0           2           0           0           0        0.00        0.00        0.00

Evaluation Scenarios

Token level evaluation for NER is too simplistic

When running machine learning models for NER, it is common to report metrics at the individual token level. This may not be the best approach, as a named entity can be made up of multiple tokens, so a full-entity accuracy would be desirable.

When comparing the golden standard annotations with the output of a NER system different scenarios might occur:

I. Surface string and entity type match

Token	Gold	Prediction
in	O	O
New	B-LOC	B-LOC
York	I-LOC	I-LOC
.	O	O

II. System hypothesized an incorrect entity

Token	Gold	Prediction
an	O	O
Awful	O	B-ORG
Headache	O	I-ORG
in	O	O

III. System misses an entity

Token	Gold	Prediction
in	O	O
Palo	B-LOC	O
Alto	I-LOC	O
,	O	O

Based on these three scenarios we have a simple classification evaluation that can be measured in terms of false positives, true positives, false negatives and false positives, and subsequently compute precision, recall and F1-score for each named-entity type.

However, this simple schema ignores the possibility of partial matches or other scenarios when the NER system gets the named-entity surface string correct but the type wrong. We might also want to evaluate these scenarios again at a full-entity level.

For example:

IV. System identifies the surface string but assigns the wrong entity type

Token	Gold	Prediction
I	O	O
live	O	O
in	O	O
Palo	B-LOC	B-ORG
Alto	I-LOC	I-ORG
,	O	O

V. System gets the boundaries of the surface string wrong

Token	Gold	Prediction
Unless	O	B-PER
Karl	B-PER	I-PER
Smith	I-PER	I-PER
resigns	O	O

VI. System gets the boundaries and entity type wrong

Token	Gold	Prediction
Unless	O	B-ORG
Karl	B-PER	I-ORG
Smith	I-PER	I-ORG
resigns	O	O

Defining evaluation metrics

How can we incorporate these described scenarios into evaluation metrics? See the original blog for a great explanation, a summary is included here.

We can define the following five metrics to consider different categories of errors:

Error type	Explanation
Correct (COR)	both are the same
Incorrect (INC)	the output of a system and the golden annotation don’t match
Partial (PAR)	system and the golden annotation are somewhat “similar” but not the same
Missing (MIS)	a golden annotation is not captured by a system
Spurious (SPU)	system produces a response which doesn’t exist in the golden annotation

These five metrics can be measured in four different ways:

Evaluation schema	Explanation
Strict	exact boundary surface string match and entity type
Exact	exact boundary match over the surface string, regardless of the type
Partial	partial boundary match over the surface string, regardless of the type
Type	some overlap between the system tagged entity and the gold annotation is required

These five errors and four evaluation schema interact in the following ways:

Scenario	Gold entity	Gold string	Pred entity	Pred string	Type	Partial	Exact	Strict
III	BRAND	tikosyn			MIS	MIS	MIS	MIS
II			BRAND	healthy	SPU	SPU	SPU	SPU
V	DRUG	warfarin	DRUG	of warfarin	COR	PAR	INC	INC
IV	DRUG	propranolol	BRAND	propranolol	INC	COR	COR	INC
I	DRUG	phenytoin	DRUG	phenytoin	COR	COR	COR	COR
VI	GROUP	contraceptives	DRUG	oral contraceptives	INC	PAR	INC	INC

Then precision, recall and f1-score are calculated for each different evaluation schema. In order to achieve data, two more quantities need to be calculated:

POSSIBLE (POS) = COR + INC + PAR + MIS = TP + FN
ACTUAL (ACT) = COR + INC + PAR + SPU = TP + FP

Then we can compute precision, recall, f1-score, where roughly describing precision is the percentage of correct named-entities found by the NER system. Recall as the percentage of the named-entities in the golden annotations that are retrieved by the NER system.

This is computed in two different ways depending on whether we want an exact match (i.e., strict and exact ) or a partial match (i.e., partial and type) scenario:

Exact Match (i.e., strict and exact )

Precision = (COR / ACT) = TP / (TP + FP)
Recall = (COR / POS) = TP / (TP+FN)

Partial Match (i.e., partial and type)

Precision = (COR + 0.5 × PAR) / ACT = TP / (TP + FP)
Recall = (COR + 0.5 × PAR)/POS = COR / ACT = TP / (TP + FN)

Putting all together:

Measure	Type	Partial	Exact	Strict
Correct	3	3	3	2
Incorrect	2	0	2	3
Partial	0	2	0	0
Missed	1	1	1	1
Spurious	1	1	1	1
Precision	0.5	0.66	0.5	0.33
Recall	0.5	0.66	0.5	0.33
F1	0.5	0.66	0.5	0.33

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 spurious:

• Spurious (SPU) : system produces a response which does not 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.

Contributing to the nervaluate package

Extending the package to accept more formats

The Evaluator accepts the following formats:

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

Additional formats can easily be added by creating a new loader class in nervaluate/loaders.py. The loader class should inherit from the DataLoader base class and implement the load method.

The load method should return a list of entity lists, where each entity is represented as a dictionary with label, start, and end keys.

The new loader can then be added to the _setup_loaders method in the Evaluator class, and can be selected with the loader argument when instantiating the Evaluator class.

Here is list of formats we intend to include.

General Contributing

Improvements, adding new features and bug fixes are welcome. If you wish to participate in the development of nervaluate please read the guidelines in the CONTRIBUTING.md file.

---

Give a ⭐️ if this project helped you!