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Comparison of part-of-speech tagging systems

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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 Italian
23,673 20,487 20,071 1,052 13,714 6,725 369
1st 86.50 90.34 44.99±1.20 38.19 72.08 76.70 34.70
Bigram (unsup, 0 iters) 88.96±1.12 88.49±1.54 47.31±1.24 81.41±5.78
Bigram (unsup, 50 iters) 91.74±1.15 91.13±1.52 48.28±1.33 81.09±5.99
Bigram (unsup, 250 iters) 91.51±1.16 90.85±1.48 48.05±1.47 80.31±6.60
Lwsw (0 iters) 92.73±0.89 92.86±0.95 43.56±1.20 83.01±5.47
Lwsw (50 iters) 92.98±0.85 93.01±1.02 45.09±1.15 82.70±5.76
Lwsw (250 iters) 92.99±0.84 93.06±1.02 45.13±1.17 82.75±5.79
CG→1st 88.05 91.10 64.01±1.04 39.81 81.56 87.99 42.90
CG→Bigram (unsup, 0 iters) 91.83±1.03 91.39±1.42 60.37±1.45 86.77±6.33
CG→Bigram (unsup, 50 iters) 93.16±1.39 92.53±1.29 60.91±1.65 87.48±6.16
CG→Bigram (unsup, 250 iters) 92.99±1.38 92.50±1.23 60.88±1.66 87.20±6.72
CG→Lwsw (0 iters) 93.17±1.08 92.72±1.09 59.93±1.46 86.60±6.20
CG→Lwsw (50 iters) 93.37±1.02 92.74±1.16 60.38±1.57 86.54±6.21
CG→Lwsw (250 iters) 93.38±1.05 92.77±1.18 60.42±1.53 86.54±6.20
Unigram model 1 93.86±1.13 93.96±0.98 63.96±0.92 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 67.51±0.67 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 67.47±0.64 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 55.26±0.87 88.07±6.50
CG→Unigram model 1 94.34±1.11 94.73±0.88 68.42±0.69 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 68.93±0.72 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 68.88±0.72 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 65.66±1.16 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 20,071 1,052 13,714 6,725 369
1st 87.86 91.82 52.56±1.53 75.93 77.72 83.00 64.47
Bigram (unsup, 0 iters) 90.35±1.17 89.95±1.45 55.27±1.63 89.72±2.06
Bigram (unsup, 50 iters) 93.17±1.21 92.63±1.40 56.40±1.70 89.35±1.99
Bigram (unsup, 250 iters) 92.94±1.22 92.35±1.33 56.13±1.87 88.45±2.51
Lwsw (0 iters) 94.18±0.91 94.40±0.77 50.88±1.54 91.51±1.22
Lwsw (50 iters) 94.44±0.81 94.54±0.83 52.67±1.46 91.14±1.62
Lwsw (250 iters) 94.44±0.79 94.60±0.84 52.72±1.50 91.20±1.64
CG→1st 89.44 92.60 74.77±1.32 79.10 87.95 95.22 79.70
CG→Bigram (unsup, 0 iters) 93.27±1.10 92.90±1.30 70.52±1.71 95.61±1.77
CG→Bigram (unsup, 50 iters) 94.62±1.49 94.05±1.13 71.15±1.94 96.41±1.38
CG→Bigram (unsup, 250 iters) 94.45±1.48 94.03±1.09 71.11±1.95 96.06±2.05
CG→Lwsw (0 iters) 94.63±1.08 94.25±0.91 70.00±1.74 95.43±1.52
CG→Lwsw (50 iters) 94.83±1.01 94.27±0.97 70.53±1.86 95.36±1.54
CG→Lwsw (250 iters) 94.84±1.03 94.30±0.99 70.58±1.81 95.36±1.53
Unigram model 1 95.33±1.05 95.51±0.84 74.72±1.43 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 78.87±1.05 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 78.82±1.06 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 64.55±1.33 97.03±1.75
CG→Unigram model 1 95.82±1.06 96.30±0.68 79.92±0.95 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 80.51±0.95 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 80.46±0.99 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 76.70±1.46 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

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