Difference between revisions of "Temporal Expression Recognition and Normalisation (State of the art)"

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(Task A: Temporal expression extraction and normalisation)
(Task A: Temporal expression extraction and normalisation)
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| Stro ̈tgen et al., 2013
 
| Stro ̈tgen et al., 2013
| 93.08%
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| 83.85
| 87.68%
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| 78.99
| 90.30%
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| 81.34
| 83.85%
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| 93.08
| 78.99%
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| 87.68
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| 90.30
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| Chambers et al., 2013
 
| Chambers et al., 2013
| 89.36%
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| 78.72
| 91.30%
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| 80.43
| 90.32%
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| 79.57
| 78.72%
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| 89.36
| 80.43%
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| 91.30
| 79.57%
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| 90.32
| 88.90%
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| 88.90
| 78.58%
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| 70.97%
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| Filannino et al., 2013
 
| Filannino et al., 2013
| 95.12%
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| 78.86
| 84.78%
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| 70.29
| 89.66%
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| 74.33
| 78.86%
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| 95.12
| 70.29%
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| 84.78
| 74.33%
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| 89.66
| 86.31%
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| 86.31
| 76.92%
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| 76.92
| 68.97%
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| 68.97
 
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| Chang et al., 2013
 
| Chang et al., 2013
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| 80.43
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| 79.57
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| 89.36
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| 91.30
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| 90.32
| 88.90%
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| 88.90
| 74.60%
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| 74.60
| 67.38%
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| 67.38
 
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| Jung et al., 2013
 
| Jung et al., 2013
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| 69.57
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| 78.69
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| 98.11
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| 75.36
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| 85.25
| 91.34%
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| 91.34
| 76.91%
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| 76.91
| 65.57%
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| 65.57
 
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| Bethard, 2013
 
| Bethard, 2013
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| 85.94
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| 79.71
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| 82.71
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| 93.75
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| 86.96
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| 90.23
| 93.33%
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| 93.33
| 71.66%
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| 71.66
| 64.66%
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| Kolya et al., 2013
 
| Kolya et al., 2013
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| 81.51
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| 70.29
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| 75.49
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| 93.28
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| 80.43
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| 86.38
| 87.39%
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| 87.39
| 73.87%
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| 73.87
| 63.81%
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| 63.81
 
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| KUL
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| KUL (2)
 
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| Kolomiyets et al., 2013
 
| Kolomiyets et al., 2013
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| 76.99
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| 63.04
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| 69.32
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| 92.92
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| 76.09
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| 83.67
| 88.56%
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| 88.56
| 75.24%
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| 75.24
| 62.95%
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| 62.95
 
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| Zavarella et al., 2013
 
| Zavarella et al., 2013
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| 52.03
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| 46.38
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| 49.04
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| 90.24
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| 80.43
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| 85.06
| 81.08%
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| 81.08
| 68.47%
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| 68.47
| 58.24%
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| 58.24
 
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Revision as of 02:31, 11 June 2013

Data sets

Performance measures

Results

The following results refers to the TempEval-3 challenge, the last evaluation exercise.

Task A: Temporal expression extraction and normalisation

The table shows the best result for each system. Different runs per system are not shown.

System name (best run) Short description Main publication Identification Normalisation Overall score Software License
Strict matching Lenient matching Accuracy
Pre. Rec. F1 Pre. Rec. F1 Type Value
HeidelTime (t) Stro ̈tgen et al., 2013 83.85 78.99 81.34 93.08 87.68 90.30 90.91 85.95 77.61
NavyTime (1,2) Chambers et al., 2013 78.72 80.43 79.57 89.36 91.30 90.32 88.90 78.58 70.97
ManTIME (4) Filannino et al., 2013 78.86 70.29 74.33 95.12 84.78 89.66 86.31 76.92 68.97
SUTime Chang et al., 2013 78.72 80.43 79.57 89.36 91.30 90.32 88.90 74.60 67.38
ATT (2) Jung et al., 2013 90.57 69.57 78.69 98.11 75.36 85.25 91.34 76.91 65.57
ClearTK (1,2) Bethard, 2013 85.94 79.71 82.71 93.75 86.96 90.23 93.33 71.66 64.66
JU-CSE Kolya et al., 2013 81.51 70.29 75.49 93.28 80.43 86.38 87.39 73.87 63.81
KUL (2) Kolomiyets et al., 2013 76.99 63.04 69.32 92.92 76.09 83.67 88.56 75.24 62.95
FSS-TimEx Zavarella et al., 2013 52.03 46.38 49.04 90.24 80.43 85.06 81.08 68.47 58.24

Task B: Event extraction and classification

Task C: Annotating relations given gold entities

Task C relation only: Annotating relations given gold entities and related pairs

Challenges

  • TempEval, Temporal Relation Identification, 2007: web page
  • TempEval-2, Evaluating Events, Time Expressions, and Temporal Relations, 2010: web page
  • TempEval-3, Evaluating Time Expressions, Events, and Temporal Relations, 2013: web page

References

  • UzZaman, N., Llorens, H., Derczynski, L., Allen, J., Verhagen, M., and Pustejovsky, J. Semeval-2013 task 1: Tempeval-3: Evaluating time expressions, events, and temporal relations. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 1–9.
  • Bethard, S. ClearTK-TimeML: A minimalist approach to tempeval 2013. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), vol. 2, Association for Computational Linguistics, Association for Computational Linguistics, pp. 10–14.
  • Stro ̈tgen, J., Zell, J., and Gertz, M. Heideltime: Tuning english and developing spanish resources for tempeval-3. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 15–19.
  • Jung, H., and Stent, A. ATT1: Temporal annotation using big windows and rich syntactic and semantic features. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 20–24.
  • Filannino, M., Brown, G., and Nenadic, G. ManTIME: Temporal expression identification and normalization in the Tempeval-3 challenge. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evalu- ation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 53–57.
  • Zavarella, V., and Tanev, H. FSS-TimEx for tempeval-3: Extracting temporal information from text. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 58–63.
  • Kolya, A. K., Kundu, A., Gupta, R., Ekbal, A., and Bandyopadhyay, S. JU_CSE: A CRF based approach to annotation of temporal expression, event and temporal relations. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 64–72.
  • Chambers, N. Navytime: Event and time ordering from raw text. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 73–77.
  • Chang, A., and Manning, C. D. SUTime: Evaluation in TempEval-3. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 78–82.
  • Kolomiyets, O., and Moens, M.-F. KUL: Data-driven approach to temporal parsing of newswire articles. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceed- ings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 83–87.
  • Laokulrat, N., Miwa, M., Tsuruoka, Y., and Chikayama, T. UTTime: Temporal relation classification using deep syntactic features. In Second Joint Conference on Lexical and Computational Se- mantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 88– 92.

See also

External links