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Loganathan Ramasamy edited this page Apr 17, 2015 · 58 revisions

Datasets


Dataset name Source
korektor-czech-130202 The current Korektor model
syn2005 Czech National Corpus (CNC) - http://hdl.handle.net/11858/00-097C-0000-0023-119E-8
syn2010 Czech National Corpus (CNC) - http://hdl.handle.net/11858/00-097C-0000-0023-119F-6

Evaluation


precision = TP / (TP + FP)

recall = TP / (TP + FN)

F1-score = 2 * (precision * recall) / (precision + recall)

Error detection

Measure Description
TP Number of words with spelling errors that the spell checker detected correctly
FP Number of words identified as spelling errors that are not actually spelling errors
TN Number of correct words that the spell checker did not flag as having spelling errors
FN Number of words with spelling errors that the spell checker did not flag as having spelling errors

Error correction

Measure Description
TP Number of words with spelling errors for which the spell checker gave the correct suggestion
FP Number of words (with/without spelling errors) for which the spell checker made suggestions, and for those, either the suggestion is not needed (in the case of non-existing errors) or the suggestion is incorrect if indeed there was an error in the original word.
TN Number of correct words that the spell checker did not flag as having spelling errors and no suggestions were made.
FN Number of words with spelling errors that the spell checker did not flag as having spelling errors or did not provide any suggestions

Results


Error detection based on varying edit distances

Dataset Max edit distance Precision Recall F1-score
kor-cz-130202 1-edit 94.7 90.8 92.7
syn2005 95.7 90.8 93.2
syn2010 94.7 89.9 92.2
kor-cz-130202 2-edit 94.1 95.4 94.8
syn2005 95.0 95.9 95.4
syn2010 94.1 95.0 94.5
kor-cz-130202 3edit 94.1 95.4 94.8
syn2005 95.0 95.9 95.4
syn2010 94.1 95.0 94.5
kor-cz-130202 4-edit 94.1 95.4 94.8
syn2005 95.0 95.9 95.4
syn2010 94.1 95.0 94.5
kor-cz-130202 5-edit 94.1 95.4 94.8
syn2005 95.0 95.9 95.4
syn2010 94.1 95.0 94.5

Note that the results are same for edit distances 2,3,4,5. This maybe due to the edit distance parameter does not really influence the error detection much.

Error correction results for varying edit distances

Item | top-1 | top-1 | top-1 | top-2 | top-2 | top-2 | top-3 | top-3 | top-3 ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ dataset | precision | recall | F1-score | precision | recall | F1-score | precision | recall | F1-score kor-cz-130202-1-ed | 85.2 | 89.9 | 87.5 | 90.9 | 90.5 | 90.7 | 93.3 | 90.7 | 92.0 syn2005-1-ed | 87.9 | 90.1 | 89.0 | 92.3 | 90.5 | 91.4 | 93.7 | 90.7 | 92.2 syn2010-1-ed | 86.0 | 89.0 | 87.5 | 91.8 | 89.6 | 90.7 | 92.3 | 89.7 | 91.0 kor-cz-130202-2-ed | 84.2 | 94.9 | 89.2 | 91.0 | 95.3 | 93.1 | 93.2 | 95.4 | 94.3 syn2005-2-ed | 86.8 | 95.5 | 91.0 | 91.8 | 95.7 | 93.7 | 93.2 | 95.8 | 94.5 syn2010-2-ed | 85.0 | 94.4 | 89.5 | 91.4 | 94.8 | 93.1 | 92.3 | 94.9 | 93.5 kor-cz-130202-3-ed | 84.2 | 94.9 | 89.2 | 91.0 | 95.3 | 93.1 | 93.2 | 95.4 | 94.3 syn2005-3-ed | 86.8 | 95.5 | 91.0 | 91.4 | 95.7 | 93.5 | 92.7 | 95.8 | 94.2 syn2010-3-ed | 85.0 | 94.4 | 89.5 | 90.9 | 94.8 | 92.8 | 91.8 | 94.8 | 93.3 kor-cz-130202-4-ed | 84.2 | 94.9 | 89.2 | 91.0 | 95.3 | 93.1 | 93.2 | 95.4 | 94.3 syn2005-4-ed | 86.8 | 95.5 | 91.0 | 91.4 | 95.7 | 93.5 | 92.7 | 95.8 | 94.2 syn2010-4-ed | 85.0 | 94.4 | 89.5 | 90.9 | 94.8 | 92.8 | 91.8 | 94.8 | 93.3 kor-cz-130202-5-ed | 84.2 | 94.9 | 89.2 | 91.0 | 95.3 | 93.1 | 93.2 | 95.4 | 94.3 syn2005-5-ed | 86.8 | 95.5 | 91.0 | 91.4 | 95.7 | 93.5 | 92.7 | 95.8 | 94.2 syn2010-5-ed | 85.0 | 94.4 | 89.5 | 90.9 | 94.8 | 92.8 | 91.8 | 94.8 | 93.3

Binarized language model comparison : KenLM Vs Korektor

Key Description
No pruning no pruning in the n-gram counts
prune_001 trigram pruning (singleton)
prune_01 trigram+bigram pruning (singleton)
prune_02 trigram+bigram pruning (singleton+ count 2 ngrams)
Ken LM parameters ARPA LM Binarized KenLM Binarized Korektor LM
No pruning 3.2G 415MB
prune_001 1.2G 884M (probing), 425M (trie) 194M
prune_01 540M 401M (probing), 202M (trie) 82M
prune_02 290M 240M (probing), 135M (trie) 46M
Spell checker error detection performance: unpruned Vs pruned LM models
Test dataset LM pruning parameters Precision Recall F1-score
Olga no_pruning 95.0 95.9 95.4
" prune_001 95.0 95.9 95.4
" prune_01 95.0 95.9 95.4
" prune_02 95.0 95.9 95.4
Spell checker error correction performance: unpruned Vs pruned LM models
-/- -/- top-1 top-1 top-1 top-2 top-2 top-2 top-3 top-3 top-3
Test dataset LM pruning parameters precision recall F1-score precision recall F1-score precision recall F1-score
Olga no_pruning 86.8 95.5 91.0 91.8 95.7 93.7 93.2 95.8 94.5
" prune_001 87.3 95.5 91.2 91.8 95.7 93.7 93.2 95.8 94.5
" prune_01 87.7 95.5 91.5 91.8 95.7 93.7 93.2 95.8 94.5
" prune_02 86.4 95.5 90.7 91.4 95.7 93.5 92.7 95.8 94.2
Diacritics error detection performance: unpruned Vs pruned LM models
Test dataset LM pruning parameters Precision Recall F1-score
dejiny no_pruning 99.5 97.9 98.7
" prune_001 99.5 97.9 98.7
" prune_01 99.5 97.9 98.7
" prune_02 99.5 97.8 98.6
lisky no_pruning 99.5 98.1 98.8
" prune_001 99.5 98.1 98.8
" prune_01 99.4 98.1 98.8
" prune_02 99.4 98.1 98.8
povesti no_pruning 98.8 94.5 96.6
" prune_001 98.7 94.5 96.6
" prune_01 98.7 94.4 96.5
" prune_02 98.7 94.4 96.5
Diacritics error correction performance: unpruned Vs pruned LM models
-/- -/- top-1 top-1 top-1 top-2 top-2 top-2 top-3 top-3 top-3
Test dataset LM pruning parameters precision recall F1-score precision recall F1-score precision recall F1-score
dejiny no_pruning 98.8 97.9 98.4 99.4 97.9 98.6 99.4 97.9 98.6
" prune_001 98.8 97.9 98.3 99.3 97.9 98.6 99.4 97.9 98.6
" prune_01 98.8 97.8 98.3 99.3 97.9 98.6 99.4 97.9 98.6
" prune_02 98.8 97.8 98.3 99.3 97.8 98.6 99.4 97.8 98.6
lisky no_pruning 98.5 98.1 98.3 99.1 98.1 98.6 99.2 98.1 98.7
" prune_001 98.5 98.1 98.3 99.1 98.1 98.6 99.2 98.1 98.7
" prune_01 98.4 98.1 98.3 99.1 98.1 98.6 99.2 98.1 98.7
" prune_02 98.4 98.1 98.2 99.1 98.1 98.6 99.2 98.1 98.7
povesti no_pruning 96.2 94.4 95.3 97.6 94.4 96.0 98.0 94.5 96.2
" prune_001 96.1 94.3 95.2 97.6 94.4 96.0 97.9 94.4 96.2
" prune_01 96.1 94.3 95.2 97.5 94.4 95.9 97.9 94.4 96.1
" prune_02 96.1 94.3 95.2 97.5 94.3 95.9 97.9 94.4 96.1
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