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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 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.

System Language
Catalan Spanish Serbo-Croatian Russian Kazakh Portuguese Swedish
23,673 20,487 20,128 10,171 4,348 6,725 239
1st 86.50, 87.86 90.29, 91.77 75.22 75.63 80.79 76.70, 83.00 72.90
CG→1st 88.05, 89.44 91.06, 92.55 79.67 79.52 86.19 87.99, 95.22 73.86
Unigram model 1 93.86±1.13, 95.33±1.05 93.93±0.97, 95.48±0.83 86.00±6.63, 94.74±2.44
CG→Unigram model 1 94.34±1.11, 95.82±1.06 94.71±0.87, 96.27±0.67 88.42±6.55, 97.42±1.76
Unigram model 2 93.90±1.09, 95.37±1.04 93.66±0.92, 95.20±0.75 87.13±6.23, 96.01±1.70
CG→Unigram model 2 94.11±1.09, 95.58±1.07 94.30±0.81, 95.86±0.59 88.64±6.13, 97.70±1.32
Unigram model 3 93.88±1.08, 95.35±1.03 93.64±0.93, 95.19±0.77 87.11±6.13, 95.99±1.52
CG→Unigram model 3 94.09±1.08, 95.56±1.05 94.28±0.80, 95.83±0.60 88.63±6.07, 97.69±1.28
Bigram (unsup, 0 iters) 88.96±1.13, 90.36±1.18 88.42±1.49, 89.88±1.40 81.41±5.78, 89.72±2.06
Bigram (unsup, 50 iters) 91.74±1.15, 93.17±1.21 91.08±1.51, 92.58±1.39 81.09±5.99, 89.35±1.99
Bigram (unsup, 250 iters) 91.52±1.15, 92.96±1.21 90.81±1.51, 92.31±1.36 80.31±6.60, 88.45±2.51
CG→Bigram (unsup, 0 iters) 91.84±1.04, 93.27±1.11 91.33±1.38, 92.84±1.26 86.77±6.33, 95.61±1.77
CG→Bigram (unsup, 50 iters) 93.16±1.39, 94.62±1.49 92.45±1.28, 93.97±1.13 87.48±6.16, 96.41±1.38
CG→Bigram (unsup, 250 iters) 92.99±1.38, 94.45±1.48 92.45±1.28, 93.97±1.13 87.20±6.72, 96.06±2.05
Bigram (sup) 96.00±0.87, 97.50±0.93 95.42±1.06, 96.99±0.86 88.07±6.50, 97.03±1.75
CG→Bigram (sup) 96.00±1.13, 97.51±1.21 94.83±1.16, 96.40±0.91 88.73±6.36, 97.78±1.52
Lwsw (0 iters) 92.73±0.89, 94.18±0.91 92.78±0.94, 94.31±0.77 83.01±5.47, 91.51±1.22
Lwsw (50 iters) 92.99±0.85, 94.44±0.81 92.93±1.02, 94.46±0.84 82.70±5.76, 91.14±1.62
Lwsw (250 iters) 92.99±0.84, 94.44±0.79 92.98±1.02, 94.51±0.86 82.75±5.79, 91.20±1.64
CG→Lwsw (0 iters) 93.17±1.08, 94.63±1.08 92.64±1.07, 94.17±0.90 86.60±6.20, 95.43±1.52
CG→Lwsw (50 iters) 93.38±1.03, 94.84±1.01 92.66±1.16, 94.19±0.97 86.54±6.21, 95.36±1.54
CG→Lwsw (250 iters) 93.38±1.05, 94.84±1.03 92.69±1.17, 94.22±1.00 86.54±6.20, 95.36±1.53
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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