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Comparison of part-of-speech tagging systems

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Apertium would like to have really good part-of-speech tagging, but in many cases falls below the state-of-the-art (around 97% tagging accuracy). This page intends to collect a comparison of tagging systems in Apertium and give some ideas of what could be done to improve them.
 
Apertium would like to have really good part-of-speech tagging, but in many cases falls below the state-of-the-art (around 97% tagging accuracy). This page intends to collect a comparison of tagging systems in Apertium and give some ideas of what could be done to improve them.
   
In the following table the two values a, b are representing tagger recall (= [true positives]/[total tokens]) and availability adjusted tagger recall (= [true positives]/[words with a correct analysis from the morphological parser]) respectively. Values of the form x±y are the sample mean and standard deviation of the results of 10-fold cross validation.
+
In the following two tables, values of the form x±y are the sample mean and standard deviation of the results of 10-fold cross validation.
  +
  +
In the following table the values represent tagger recall (= [true positives]/[total tokens]):
   
 
{|class=wikitable
 
{|class=wikitable
Line 10: Line 10:
 
! Catalan !! Spanish !! Serbo-Croatian !! Russian !! Kazakh !! Portuguese !! Swedish
 
! Catalan !! Spanish !! Serbo-Croatian !! Russian !! Kazakh !! Portuguese !! Swedish
 
|-
 
|-
! <small>23,673</small> !! <small>20,487</small> !! <small>20,128</small> !! <small>10,171</small> !! <small>4,348</small> !! <small>6,725</small> !! <small>239</small>
+
! <small>23,673</small> !! <small>20,487</small> !! <small>20,128</small> !! <small>1,052</small> !! <small>13,714</small> !! <small>6,725</small> !! <small>369</small>
 
|-
 
|-
| '''1st''' ||align=right| 86.50, 87.86 ||align=right| 90.29, 91.77 ||align=right| 75.22 ||align=right| 75.63 ||align=right| 80.79||align=right| 76.70, 83.00 ||align=right| 72.90
+
| '''1st''' ||align=right| 86.50 ||align=right| 90.34 ||||align=right| 38.19 ||align=right| 72.08 ||align=right| 76.70 ||align=right| 34.70
  +
|-
  +
| '''Bigram (unsup, 0 iters)''' ||align=right| 88.96±1.12 ||align=right| 88.49±1.54 ||||||||align=right| 81.41±5.78
  +
|-
  +
| '''Bigram (unsup, 50 iters)''' ||align=right| 91.74±1.15 ||align=right| 91.13±1.52 ||||||||align=right| 81.09±5.99
  +
|-
  +
| '''Bigram (unsup, 250 iters)''' ||align=right| 91.51±1.16 ||align=right| 90.85±1.48 ||||||||align=right| 80.31±6.60
  +
|-
  +
| '''Lwsw (0 iters)''' ||align=right| 92.73±0.89 ||align=right| 92.86±0.95 ||||||||align=right| 83.01±5.47
  +
|-
  +
| '''Lwsw (50 iters)''' ||align=right| 92.98±0.85 ||align=right| 93.01±1.02 ||||||||align=right| 82.70±5.76
  +
|-
  +
| '''Lwsw (250 iters)''' ||align=right| 92.99±0.84 ||align=right| 93.06±1.02 ||||||||align=right| 82.75±5.79
  +
|-
  +
| '''CG→1st''' ||align=right| 88.05 ||align=right| 91.10 ||||align=right| 39.81 ||align=right| 81.56 ||align=right| 87.99 ||align=right| 42.90
  +
|-
  +
| '''CG→Bigram (unsup, 0 iters)''' ||align=right| 91.83±1.03 ||align=right| 91.39±1.42 ||||||||align=right| 86.77±6.33
  +
|-
  +
| '''CG→Bigram (unsup, 50 iters)''' ||align=right| 93.16±1.39 ||align=right| 92.53±1.29 ||||||||align=right| 87.48±6.16
  +
|-
  +
| '''CG→Bigram (unsup, 250 iters)''' ||align=right| 92.99±1.38 ||align=right| 92.50±1.23 ||||||||align=right| 87.20±6.72
  +
|-
  +
| '''CG→Lwsw (0 iters)''' ||align=right| 93.17±1.08 ||align=right| 92.72±1.09 ||||||||align=right| 86.60±6.20
  +
|-
  +
| '''CG→Lwsw (50 iters)''' ||align=right| 93.37±1.02 ||align=right| 92.74±1.16 ||||||||align=right| 86.54±6.21
  +
|-
  +
| '''CG→Lwsw (250 iters)''' ||align=right| 93.38±1.05 ||align=right| 92.77±1.18 ||||||||align=right| 86.54±6.20
  +
|-
  +
| '''Unigram model 1''' ||align=right| 93.86±1.13 ||align=right| 93.96±0.98 ||||align=right| 39.11±8.91 ||align=right| 80.63±3.87 ||align=right| 86.00±6.63 ||align=right| 46.48±5.78
  +
|-
  +
| '''Unigram model 2''' ||align=right| 93.90±1.09 ||align=right| 93.69±0.94 ||||align=right| 40.36±8.59 ||align=right| 82.19±3.70 ||align=right| 87.13±6.23 ||align=right| 47.12±8.29
  +
|-
  +
| '''Unigram model 3''' ||align=right| 93.88±1.08 ||align=right| 93.67±0.94 ||||align=right| 40.36±8.59 ||align=right| 82.45±3.80 ||align=right| 87.11±6.13 ||align=right| 47.12±8.29
  +
|-
  +
| '''Bigram (sup)''' ||align=right| 96.00±0.87 ||align=right| 95.47±1.07 ||||||||align=right| 88.07±6.50
  +
|-
  +
| '''CG→Unigram model 1''' ||align=right| 94.34±1.11 ||align=right| 94.73±0.88 ||||align=right| 40.71±9.39 ||align=right| 84.54±3.29 ||align=right| 88.42±6.55 ||align=right| 46.84±5.48
  +
|-
  +
| '''CG→Unigram model 2''' ||align=right| 94.11±1.09 ||align=right| 94.33±0.82 ||||align=right| 41.43±9.21 ||align=right| 84.62±3.47 ||align=right| 88.64±6.13 ||align=right| 47.07±7.39
  +
|-
  +
| '''CG→Unigram model 3''' ||align=right| 94.09±1.08 ||align=right| 94.31±0.81 ||||align=right| 41.43±9.21 ||align=right| 84.71±3.54 ||align=right| 88.63±6.07 ||align=right| 47.07±7.39
  +
|-
  +
| '''CG→Bigram (sup)''' ||align=right| 96.00±1.13 ||align=right| 94.88±1.18 ||||||||align=right| 88.73±6.36
  +
|}
  +
  +
In the following table the values represent availability adjusted tagger recall (= [true positives]/[words with a correct analysis from the morphological parser]). This data is also available in box plot form [https://frankier.github.io/apertium-tagger-visualisation/taggers.html here]:
  +
  +
{|class=wikitable
  +
!rowspan=3|System !!colspan=7|Language
  +
|-
  +
! Catalan !! Spanish !! Serbo-Croatian !! Russian !! Kazakh !! Portuguese !! Swedish
 
|-
 
|-
| '''CG→1st''' ||align=right| 88.05, 89.44 ||align=right| 91.06, 92.55 ||align=right| 79.67 ||align=right| 79.52 ||align=right| 86.19 ||align=right| 87.99, 95.22 ||align=right| 73.86
+
! <small>23,673</small> !! <small>20,487</small> !! <small>20,128</small> !! <small>1,052</small> !! <small>13,714</small> !! <small>6,725</small> !! <small>369</small>
 
|-
 
|-
| '''Unigram model 1''' ||align=right| 93.86±1.13, 95.33±1.05 ||align=right| 93.93±0.97, 95.48±0.83 ||||||||align=right| 86.00±6.63, 94.74±2.44
+
| '''1st''' ||align=right| 87.86 ||align=right| 91.82 ||||align=right| 75.93 ||align=right| 77.72 ||align=right| 83.00 ||align=right| 64.47
 
|-
 
|-
| '''CG→Unigram model 1''' ||align=right| 94.34±1.11, 95.82±1.06 ||align=right| 94.71±0.87, 96.27±0.67 ||||||||align=right| 88.42±6.55, 97.42±1.76
+
| '''Bigram (unsup, 0 iters)''' ||align=right| 90.35±1.17 ||align=right| 89.95±1.45 ||||||||align=right| 89.72±2.06
 
|-
 
|-
| '''Unigram model 2''' ||align=right| 93.90±1.09, 95.37±1.04 ||align=right| 93.66±0.92, 95.20±0.75 ||||||||align=right| 87.13±6.23, 96.01±1.70
+
| '''Bigram (unsup, 50 iters)''' ||align=right| 93.17±1.21 ||align=right| 92.63±1.40 ||||||||align=right| 89.35±1.99
 
|-
 
|-
| '''CG→Unigram model 2''' ||align=right| 94.11±1.09, 95.58±1.07 ||align=right| 94.30±0.81, 95.86±0.59 ||||||||align=right| 88.64±6.13, 97.70±1.32
+
| '''Bigram (unsup, 250 iters)''' ||align=right| 92.94±1.22 ||align=right| 92.35±1.33 ||||||||align=right| 88.45±2.51
 
|-
 
|-
| '''Unigram model 3''' ||align=right| 93.88±1.08, 95.35±1.03 ||align=right| 93.64±0.93, 95.19±0.77 ||||||||align=right| 87.11±6.13, 95.99±1.52
+
| '''Lwsw (0 iters)''' ||align=right| 94.18±0.91 ||align=right| 94.40±0.77 ||||||||align=right| 91.51±1.22
 
|-
 
|-
| '''CG→Unigram model 3''' ||align=right| 94.09±1.08, 95.56±1.05 ||align=right| 94.28±0.80, 95.83±0.60 ||||||||align=right| 88.63±6.07, 97.69±1.28
+
| '''Lwsw (50 iters)''' ||align=right| 94.44±0.81 ||align=right| 94.54±0.83 ||||||||align=right| 91.14±1.62
 
|-
 
|-
| '''Bigram (unsup, 0 iters)''' ||align=right| 88.96±1.13, 90.36±1.18 ||align=right| 88.42±1.49, 89.88±1.40 ||||||||align=right| 81.41±5.78, 89.72±2.06
+
| '''Lwsw (250 iters)''' ||align=right| 94.44±0.79 ||align=right| 94.60±0.84 ||||||||align=right| 91.20±1.64
 
|-
 
|-
| '''Bigram (unsup, 50 iters)''' ||align=right| 91.74±1.15, 93.17±1.21 ||align=right| 91.08±1.51, 92.58±1.39 ||||||||align=right| 81.09±5.99, 89.35±1.99
+
| '''CG→1st''' ||align=right| 89.44 ||align=right| 92.60 ||||align=right| 79.10 ||align=right| 87.95 ||align=right| 95.22 ||align=right| 79.70
 
|-
 
|-
| '''Bigram (unsup, 250 iters)''' ||align=right| 91.52±1.15, 92.96±1.21 ||align=right| 90.81±1.51, 92.31±1.36 ||||||||align=right| 80.31±6.60, 88.45±2.51
+
| '''CG→Bigram (unsup, 0 iters)''' ||align=right| 93.27±1.10 ||align=right| 92.90±1.30 ||||||||align=right| 95.61±1.77
 
|-
 
|-
| '''CG→Bigram (unsup, 0 iters)''' ||align=right| 91.84±1.04, 93.27±1.11 ||align=right| 91.33±1.38, 92.84±1.26 ||||||||align=right| 86.77±6.33, 95.61±1.77
+
| '''CG→Bigram (unsup, 50 iters)''' ||align=right| 94.62±1.49 ||align=right| 94.05±1.13 ||||||||align=right| 96.41±1.38
 
|-
 
|-
| '''CG→Bigram (unsup, 50 iters)''' ||align=right| 93.16±1.39, 94.62±1.49 ||align=right| 92.45±1.28, 93.97±1.13 ||||||||align=right| 87.48±6.16, 96.41±1.38
+
| '''CG→Bigram (unsup, 250 iters)''' ||align=right| 94.45±1.48 ||align=right| 94.03±1.09 ||||||||align=right| 96.06±2.05
 
|-
 
|-
| '''CG→Bigram (unsup, 250 iters)''' ||align=right| 92.99±1.38, 94.45±1.48 ||align=right| 92.45±1.28, 93.97±1.13 ||||||||align=right| 87.20±6.72, 96.06±2.05
+
| '''CG→Lwsw (0 iters)''' ||align=right| 94.63±1.08 ||align=right| 94.25±0.91 ||||||||align=right| 95.43±1.52
 
|-
 
|-
| '''Bigram (sup)''' ||align=right| 96.00±0.87, 97.50±0.93 ||align=right| 95.42±1.06, 96.99±0.86 ||||||||align=right| 88.07±6.50, 97.03±1.75
+
| '''CG→Lwsw (50 iters)''' ||align=right| 94.83±1.01 ||align=right| 94.27±0.97 ||||||||align=right| 95.36±1.54
 
|-
 
|-
| '''CG→Bigram (sup)''' ||align=right| 96.00±1.13, 97.51±1.21 ||align=right| 94.83±1.16, 96.40±0.91 ||||||||align=right| 88.73±6.36, 97.78±1.52
+
| '''CG→Lwsw (250 iters)''' ||align=right| 94.84±1.03 ||align=right| 94.30±0.99 ||||||||align=right| 95.36±1.53
 
|-
 
|-
| '''Lwsw (0 iters)''' ||align=right| 92.73±0.89, 94.18±0.91 ||align=right| 92.78±0.94, 94.31±0.77 ||||||||align=right| 83.01±5.47, 91.51±1.22
+
| '''Unigram model 1''' ||align=right| 95.33±1.05 ||align=right| 95.51±0.84 ||||align=right| 77.54±6.51 ||align=right| 87.03±3.03 ||align=right| 94.74±2.44 ||align=right| 89.26±7.32
 
|-
 
|-
| '''Lwsw (50 iters)''' ||align=right| 92.99±0.85, 94.44±0.81 ||align=right| 92.93±1.02, 94.46±0.84 ||||||||align=right| 82.70±5.76, 91.14±1.62
+
| '''Unigram model 2''' ||align=right| 95.37±1.04 ||align=right| 95.23±0.77 ||||align=right| 80.06±6.11 ||align=right| 88.72±2.76 ||align=right| 96.01±1.70 ||align=right| 89.82±7.70
 
|-
 
|-
| '''Lwsw (250 iters)''' ||align=right| 92.99±0.84, 94.44±0.79 ||align=right| 92.98±1.02, 94.51±0.86 ||||||||align=right| 82.75±5.79, 91.20±1.64
+
| '''Unigram model 3''' ||align=right| 95.35±1.03 ||align=right| 95.22±0.79 ||||align=right| 80.06±6.11 ||align=right| 88.99±2.83 ||align=right| 95.99±1.52 ||align=right| 89.82±7.70
 
|-
 
|-
| '''CG→Lwsw (0 iters)''' ||align=right| 93.17±1.08, 94.63±1.08 ||align=right| 92.64±1.07, 94.17±0.90 ||||||||align=right| 86.60±6.20, 95.43±1.52
+
| '''Bigram (sup)''' ||align=right| 97.50±0.93 ||align=right| 97.04±0.86 ||||||||align=right| 97.03±1.75
 
|-
 
|-
| '''CG→Lwsw (50 iters)''' ||align=right| 93.38±1.03, 94.84±1.01 ||align=right| 92.66±1.16, 94.19±0.97 ||||||||align=right| 86.54±6.21, 95.36±1.54
+
| '''CG→Unigram model 1''' ||align=right| 95.82±1.06 ||align=right| 96.30±0.68 ||||align=right| 80.56±6.70 ||align=right| 91.25±2.01 ||align=right| 97.42±1.76 ||align=right| 90.00±6.99
 
|-
 
|-
| '''CG→Lwsw (250 iters)''' ||align=right| 93.38±1.05, 94.84±1.03 ||align=right| 92.69±1.17, 94.22±1.00 ||||||||align=right| 86.54±6.20, 95.36±1.53
+
| '''CG→Unigram model 2''' ||align=right| 95.58±1.07 ||align=right| 95.89±0.59 ||||align=right| 82.06±6.50 ||align=right| 91.33±2.15 ||align=right| 97.70±1.32 ||align=right| 89.97±7.50
 
|-
 
|-
| '''kaz-tagger''' ||
+
| '''CG→Unigram model 3''' ||align=right| 95.56±1.05 ||align=right| 95.86±0.60 ||||align=right| 82.06±6.50 ||align=right| 91.43±2.26 ||align=right| 97.69±1.28 ||align=right| 89.97±7.50
 
|-
 
|-
| '''CG→kaz-tagger''' ||
+
| '''CG→Bigram (sup)''' ||align=right| 97.51±1.21 ||align=right| 96.45±0.93 ||||||||align=right| 97.78±1.52
 
|}
 
|}
   

Revision as of 22:02, 22 June 2016

Contents

Apertium would like to have really good part-of-speech tagging, but in many cases falls below the state-of-the-art (around 97% tagging accuracy). This page intends to collect a comparison of tagging systems in Apertium and give some ideas of what could be done to improve them.

In the following two tables, values of the form x±y are the sample mean and standard deviation of the results of 10-fold cross validation.

In the following table the values represent tagger recall (= [true positives]/[total tokens]):

System Language
Catalan Spanish Serbo-Croatian Russian Kazakh Portuguese Swedish
23,673 20,487 20,128 1,052 13,714 6,725 369
1st 86.50 90.34 38.19 72.08 76.70 34.70
Bigram (unsup, 0 iters) 88.96±1.12 88.49±1.54 81.41±5.78
Bigram (unsup, 50 iters) 91.74±1.15 91.13±1.52 81.09±5.99
Bigram (unsup, 250 iters) 91.51±1.16 90.85±1.48 80.31±6.60
Lwsw (0 iters) 92.73±0.89 92.86±0.95 83.01±5.47
Lwsw (50 iters) 92.98±0.85 93.01±1.02 82.70±5.76
Lwsw (250 iters) 92.99±0.84 93.06±1.02 82.75±5.79
CG→1st 88.05 91.10 39.81 81.56 87.99 42.90
CG→Bigram (unsup, 0 iters) 91.83±1.03 91.39±1.42 86.77±6.33
CG→Bigram (unsup, 50 iters) 93.16±1.39 92.53±1.29 87.48±6.16
CG→Bigram (unsup, 250 iters) 92.99±1.38 92.50±1.23 87.20±6.72
CG→Lwsw (0 iters) 93.17±1.08 92.72±1.09 86.60±6.20
CG→Lwsw (50 iters) 93.37±1.02 92.74±1.16 86.54±6.21
CG→Lwsw (250 iters) 93.38±1.05 92.77±1.18 86.54±6.20
Unigram model 1 93.86±1.13 93.96±0.98 39.11±8.91 80.63±3.87 86.00±6.63 46.48±5.78
Unigram model 2 93.90±1.09 93.69±0.94 40.36±8.59 82.19±3.70 87.13±6.23 47.12±8.29
Unigram model 3 93.88±1.08 93.67±0.94 40.36±8.59 82.45±3.80 87.11±6.13 47.12±8.29
Bigram (sup) 96.00±0.87 95.47±1.07 88.07±6.50
CG→Unigram model 1 94.34±1.11 94.73±0.88 40.71±9.39 84.54±3.29 88.42±6.55 46.84±5.48
CG→Unigram model 2 94.11±1.09 94.33±0.82 41.43±9.21 84.62±3.47 88.64±6.13 47.07±7.39
CG→Unigram model 3 94.09±1.08 94.31±0.81 41.43±9.21 84.71±3.54 88.63±6.07 47.07±7.39
CG→Bigram (sup) 96.00±1.13 94.88±1.18 88.73±6.36

In the following table the values represent availability adjusted tagger recall (= [true positives]/[words with a correct analysis from the morphological parser]). This data is also available in box plot form here:

System Language
Catalan Spanish Serbo-Croatian Russian Kazakh Portuguese Swedish
23,673 20,487 20,128 1,052 13,714 6,725 369
1st 87.86 91.82 75.93 77.72 83.00 64.47
Bigram (unsup, 0 iters) 90.35±1.17 89.95±1.45 89.72±2.06
Bigram (unsup, 50 iters) 93.17±1.21 92.63±1.40 89.35±1.99
Bigram (unsup, 250 iters) 92.94±1.22 92.35±1.33 88.45±2.51
Lwsw (0 iters) 94.18±0.91 94.40±0.77 91.51±1.22
Lwsw (50 iters) 94.44±0.81 94.54±0.83 91.14±1.62
Lwsw (250 iters) 94.44±0.79 94.60±0.84 91.20±1.64
CG→1st 89.44 92.60 79.10 87.95 95.22 79.70
CG→Bigram (unsup, 0 iters) 93.27±1.10 92.90±1.30 95.61±1.77
CG→Bigram (unsup, 50 iters) 94.62±1.49 94.05±1.13 96.41±1.38
CG→Bigram (unsup, 250 iters) 94.45±1.48 94.03±1.09 96.06±2.05
CG→Lwsw (0 iters) 94.63±1.08 94.25±0.91 95.43±1.52
CG→Lwsw (50 iters) 94.83±1.01 94.27±0.97 95.36±1.54
CG→Lwsw (250 iters) 94.84±1.03 94.30±0.99 95.36±1.53
Unigram model 1 95.33±1.05 95.51±0.84 77.54±6.51 87.03±3.03 94.74±2.44 89.26±7.32
Unigram model 2 95.37±1.04 95.23±0.77 80.06±6.11 88.72±2.76 96.01±1.70 89.82±7.70
Unigram model 3 95.35±1.03 95.22±0.79 80.06±6.11 88.99±2.83 95.99±1.52 89.82±7.70
Bigram (sup) 97.50±0.93 97.04±0.86 97.03±1.75
CG→Unigram model 1 95.82±1.06 96.30±0.68 80.56±6.70 91.25±2.01 97.42±1.76 90.00±6.99
CG→Unigram model 2 95.58±1.07 95.89±0.59 82.06±6.50 91.33±2.15 97.70±1.32 89.97±7.50
CG→Unigram model 3 95.56±1.05 95.86±0.60 82.06±6.50 91.43±2.26 97.69±1.28 89.97±7.50
CG→Bigram (sup) 97.51±1.21 96.45±0.93 97.78±1.52

In the following table, the intervals represent the [low, high] values from 10-fold cross validation.

Language Corpus System
Sent Tok Amb 1st CG+1st Unigram CG+Unigram apertium-tagger CG+apertium-tagger
Catalan 1,413 24,144  ? 81.85 83.96 [75.65, 78.46] [87.76, 90.48] [94.16, 96.28] [93.92, 96.16]
Spanish 1,271 21,247  ? 86.18 86.71 [78.20, 80.06] [87.72, 90.27] [90.15, 94.86] [91.84, 93.70]
Serbo-Croatian 1,190 20,128  ? 75.22 79.67 [75.36, 78.79] [75.36, 77.28]
Russian 451 10,171  ? 75.63 79.52 [70.49, 72.94] [74.68, 78.65] n/a n/a
Kazakh 403 4,348  ? 80.79 86.19 [84.36, 87.79] [85.56, 88.72] n/a n/a
Portuguese 119 3,823  ? 72.54 87.34 [77.10, 87.72] [84.05, 91.96]
Swedish 11 239  ? 72.90 73.86 [56.00, 82.97]

Sent = sentences, Tok = tokens, Amb = average ambiguity from the morphological analyser

Systems

  • 1st: Selects the first analysis from the morphological analyser
  • CG: Uses the CG (from the monolingual language package in languages) to preprocess the input.
  • Unigram: Lexicalised unigram tagger
  • apertium-tagger: Uses the bigram HMM tagger included with Apertium.

Corpora

The tagged corpora used in the experiments are found in the monolingual packages in languages, under the texts/ subdirectory.

Todo

Personal tools