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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 intervals represent the [low, high] values from 10-fold cross validation.
+
In the following table values of the form x±y are the sample mean and standard deviation of the results of 10-fold cross validation.
   
 
{|class=wikitable
 
{|class=wikitable
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| '''CG→1st''' ||align=right| 83.79 ||align=right| 86.71 ||align=right| 79.67 ||align=right| 79.52 ||align=right| 86.19 ||align=right| 87.34 ||align=right| 73.86
 
| '''CG→1st''' ||align=right| 83.79 ||align=right| 86.71 ||align=right| 79.67 ||align=right| 79.52 ||align=right| 86.19 ||align=right| 87.34 ||align=right| 73.86
 
|-
 
|-
| '''Unigram model 1''' ||align=right| [91.72±1.37]
+
| '''Unigram model 1''' ||align=right| 91.72±1.37
 
|-
 
|-
| '''CG→Unigram model 1''' ||align=right| [92.37±1.33]
+
| '''CG→Unigram model 1''' ||align=right| 92.37±1.33
 
|-
 
|-
| '''Unigram model 2''' ||align=right| [91.78±1.30]
+
| '''Unigram model 2''' ||align=right| 91.78±1.30
 
|-
 
|-
| '''CG→Unigram model 2''' ||align=right| [92.06±1.30]
+
| '''CG→Unigram model 2''' ||align=right| 92.06±1.30
 
|-
 
|-
| '''Unigram model 3''' ||align=right| [91.74±1.29]
+
| '''Unigram model 3''' ||align=right| 91.74±1.29
 
|-
 
|-
| '''CG→Unigram model 3''' ||align=right| [92.03±1.29]
+
| '''CG→Unigram model 3''' ||align=right| 92.03±1.29
 
|-
 
|-
| '''Bigram (unsup, 0 iters)''' ||align=right| [85.05±1.22]
+
| '''Bigram (unsup, 0 iters)''' ||align=right| 85.05±1.22
 
|-
 
|-
| '''Bigram (unsup, 50 iters)''' ||align=right| [88.81±1.36]
+
| '''Bigram (unsup, 50 iters)''' ||align=right| 88.81±1.36
 
|-
 
|-
| '''Bigram (unsup, 250 iters)''' ||align=right| [88.53±1.35]
+
| '''Bigram (unsup, 250 iters)''' ||align=right| 88.53±1.35
 
|-
 
|-
| '''CG→Bigram (unsup, 0 iters)''' ||align=right| [88.96±1.21]
+
| '''CG→Bigram (unsup, 0 iters)''' ||align=right| 88.96±1.21
 
|-
 
|-
| '''CG→Bigram (unsup, 50 iters)''' ||align=right| [90.77±1.68]
+
| '''CG→Bigram (unsup, 50 iters)''' ||align=right| 90.77±1.68
 
|-
 
|-
| '''CG→Bigram (unsup, 250 iters)''' ||align=right| [90.54±1.67]
+
| '''CG→Bigram (unsup, 250 iters)''' ||align=right| 90.54±1.67
 
|-
 
|-
| '''Bigram (sup, 0 iters)''' ||align=right| [94.60±1.06]
+
| '''Bigram (sup, 0 iters)''' ||align=right| 94.60±1.06
 
|-
 
|-
| '''Bigram (sup, 50 iters)''' ||align=right| [91.82±1.08]
+
| '''Bigram (sup, 50 iters)''' ||align=right| 91.82±1.08
 
|-
 
|-
| '''Bigram (sup, 250 iters)''' ||align=right| [91.43±1.29]
+
| '''Bigram (sup, 250 iters)''' ||align=right| 91.43±1.29
 
|-
 
|-
| '''CG→Bigram (sup, 0 iters)''' ||align=right| [94.62±1.38]
+
| '''CG→Bigram (sup, 0 iters)''' ||align=right| 94.62±1.38
 
|-
 
|-
| '''CG→Bigram (sup, 50 iters)''' ||align=right| [92.31±1.28]
+
| '''CG→Bigram (sup, 50 iters)''' ||align=right| 92.31±1.28
 
|-
 
|-
| '''CG→Bigram (sup, 250 iters)''' ||align=right| [92.02±1.43]
+
| '''CG→Bigram (sup, 250 iters)''' ||align=right| 92.02±1.43
 
|-
 
|-
| '''Lwsw (0 iters)''' ||align=right| [90.16±1.00]
+
| '''Lwsw (0 iters)''' ||align=right| 90.16±1.00
 
|-
 
|-
| '''Lwsw (50 iters)''' ||align=right| [90.51±0.98]
+
| '''Lwsw (50 iters)''' ||align=right| 90.51±0.98
 
|-
 
|-
| '''Lwsw (250 iters)''' ||align=right| [90.51±0.98]
+
| '''Lwsw (250 iters)''' ||align=right| 90.51±0.98
 
|-
 
|-
| '''CG→Lwsw (0 iters)''' ||align=right| [90.78±1.26]
+
| '''CG→Lwsw (0 iters)''' ||align=right| 90.78±1.26
 
|-
 
|-
| '''CG→Lwsw (50 iters)''' ||align=right| [91.05±1.21]
+
| '''CG→Lwsw (50 iters)''' ||align=right| 91.05±1.21
 
|-
 
|-
| '''CG→Lwsw (250 iters)''' ||align=right| [91.06±1.25]
+
| '''CG→Lwsw (250 iters)''' ||align=right| 91.06±1.25
 
|-
 
|-
 
| '''kaz-tagger''' ||
 
| '''kaz-tagger''' ||
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| '''CG→kaz-tagger''' ||
 
| '''CG→kaz-tagger''' ||
 
|}
 
|}
  +
  +
In the following table, the intervals represent the [low, high] values from 10-fold cross validation.
   
 
{|class=wikitable
 
{|class=wikitable

Revision as of 19:13, 24 May 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 table values of the form x±y are the sample mean and standard deviation of the results of 10-fold cross validation.

System Language
Catalan Spanish Serbo-Croatian Russian Kazakh Portuguese Swedish
24,144 21,247 20,128 10,171 4,348 3,823 239
1st 81.66 86.18 75.22 75.63 80.79 72.54 72.90
CG→1st 83.79 86.71 79.67 79.52 86.19 87.34 73.86
Unigram model 1 91.72±1.37
CG→Unigram model 1 92.37±1.33
Unigram model 2 91.78±1.30
CG→Unigram model 2 92.06±1.30
Unigram model 3 91.74±1.29
CG→Unigram model 3 92.03±1.29
Bigram (unsup, 0 iters) 85.05±1.22
Bigram (unsup, 50 iters) 88.81±1.36
Bigram (unsup, 250 iters) 88.53±1.35
CG→Bigram (unsup, 0 iters) 88.96±1.21
CG→Bigram (unsup, 50 iters) 90.77±1.68
CG→Bigram (unsup, 250 iters) 90.54±1.67
Bigram (sup, 0 iters) 94.60±1.06
Bigram (sup, 50 iters) 91.82±1.08
Bigram (sup, 250 iters) 91.43±1.29
CG→Bigram (sup, 0 iters) 94.62±1.38
CG→Bigram (sup, 50 iters) 92.31±1.28
CG→Bigram (sup, 250 iters) 92.02±1.43
Lwsw (0 iters) 90.16±1.00
Lwsw (50 iters) 90.51±0.98
Lwsw (250 iters) 90.51±0.98
CG→Lwsw (0 iters) 90.78±1.26
CG→Lwsw (50 iters) 91.05±1.21
CG→Lwsw (250 iters) 91.06±1.25
kaz-tagger
CG→kaz-tagger

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

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