PYEVALB

EVEVALB is a python version of Evalb which is used to score the bracket tree banks.

Installation

pip install PYEVALB

Examples

Score two corpus

from PYEVALB import scorer

s = scorer.Scorer()
gold_path = 'gold_corpus.txt'
test_path = 'test_corpus.txt'
result_path = 'result.txt'

s.evalb(gold_path, test_path, result_path)

And the result would be:

ID | length | state | recall | prec | matched_brackets | gold_brackets | test_brackets | cross_brackets | words | correct_tags | tag_accracy 

---:------:-----:--------------:---------------:-------------: 44| 0| 0.57| 0.61| 31| 54| 51| 16| 44| 43| 0.98

1| 13| 0| 0.64| 0.60| 9| 14| 15| 3| 13| 12| 0.92 2| 29| 0| 0.97| 0.97| 29| 30| 30| 0| 29| 29| 1.00 3| 20| 0| 0.80| 0.80| 20| 25| 25| 4| 20| 20| 1.00 4| 19| 0| 0.91| 1.00| 21| 23| 21| 0| 19| 19| 1.00 5| 71| 0| 0.67| 0.68| 52| 78| 77| 15| 71| 65| 0.92 6| 16| 0| 0.61| 0.69| 11| 18| 16| 0| 16| 14| 0.88 7| 27| 0| 0.92| 0.96| 24| 26| 25| 0| 27| 26| 0.96 8| 19| 0| 1.00| 1.00| 20| 20| 20| 0| 19| 19| 1.00 9| 41| 0| 0.80| 0.78| 32| 40| 41| 5| 41| 39| 0.95

================================================================================================================================================= Number of sentence: 10.00 Number of Error sentence: 0.00 Number of Skip sentence: 0.00 Number of Valid sentence: 10.00 Bracketing Recall: 75.91 Bracketing Precision: 77.57 Bracketing FMeasure: 76.73 Complete match: 10.00 Average crossing: 4.30 No crossing: 50.00 Tagging accuracy: 95.65

Score two trees

from PYEVALB import scorer
from PYEVALB import parser

gold = '(IP (NP (PN 这里)) (VP (ADVP (AD 便)) (VP (VV 产生) (IP (NP (QP (CD 一) (CLP (M 个))) (DNP (NP (JJ 结构性)) (DEG 的)) (NP (NN 盲点))) (PU ：) (IP (VP (VV 臭味相投) (PU ，) (VV 物以类聚)))))) (PU 。))'

test = '(IP (IP (NP (PN 这里)) (VP (ADVP (AD 便)) (VP (VV 产生) (NP (QP (CD 一) (CLP (M 个))) (DNP (ADJP (JJ 结构性)) (DEG 的)) (NP (NN 盲点)))))) (PU ：) (IP (NP (NN 臭味相投)) (PU ，) (VP (VV 物以类聚))) (PU 。))'

gold_tree = parser.create_from_bracket_string(gold)
test_tree = parser.create_from_bracket_string(test)

s = scorer.Scorer()
result = s.score_trees(gold_tree, test_tree)

print('Recall =' + str(result.recall))
print('Precision =' + str(result.prec))

And the result is:

Recall = 64.29
Precision =  56.25

TODO

1. Remove the dependency of pytablewriter
2. Add more configurations, such as limiting the length of sentence.
3. Add docs