Difference between revisions of "Temporal Information Extraction (State of the art)"

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| <ref name="Velupillai-2015">Sumithra Velupillai, Danielle L Mowery, Samir Abdelrahman, Lee Christensen and Wendy Chapman. [http://www.aclweb.org/anthology/S15-2137 BluLab: Temporal Information Extraction for the 2015 Clinical TempEval Challenge]. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), (Denver, Colorado, June 2015), Association for Computational Linguistics, pp. 815-819.</ref>
 
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====Temporal relations====
 
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Latest revision as of 20:45, 9 June 2015

TempEval 2007

  • TempEval, Temporal Relation Identification, 2007: web page

TempEval 2010

  • TempEval-2, Evaluating Events, Time Expressions, and Temporal Relations, 2010: web page

TempEval 2013

  • TempEval-3, Evaluating Time Expressions, Events, and Temporal Relations, 2013: web page

Performance measures

Results

Tables show the best result for each system. Lower scoring runs for the same system are not shown.

Task A: Temporal expression extraction and normalisation

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) rule-based [1] 83.85 78.99 81.34 93.08 87.68 90.30 90.91 85.95 77.61 Download GNU GPL v3
NavyTime (1,2) rule-based [2] 78.72 80.43 79.57 89.36 91.30 90.32 88.90 78.58 70.97 - -
ManTIME (4) CRF, probabilistic post-processing pipeline, rule-based normaliser [3] 78.86 70.29 74.33 95.12 84.78 89.66 86.31 76.92 68.97 Demo & Download GNU GPL v2
SUTime deterministic rule-based [4] 78.72 80.43 79.57 89.36 91.30 90.32 88.90 74.60 67.38 Demo & Download GNU GPL v2
ATT (2) MaxEnt, third party normalisers [5] 90.57 69.57 78.69 98.11 75.36 85.25 91.34 76.91 65.57 - -
ClearTK (1,2) SVM, Logistic Regression, third party normaliser [6] 85.94 79.71 82.71 93.75 86.96 90.23 93.33 71.66 64.66 Download BSD-3 Clause
JU-CSE CRF, rule-based normaliser [7] 81.51 70.29 75.49 93.28 80.43 86.38 87.39 73.87 63.81 - -
KUL (2) Logistic regression, post-processing, rule-based normaliser [8] 76.99 63.04 69.32 92.92 76.09 83.67 88.56 75.24 62.95 - -
FSS-TimEx rule-based [9] 52.03 46.38 49.04 90.24 80.43 85.06 81.08 68.47 58.24 - -

Task B: Event extraction and classification

System name (best run) Short description Main publication Identification Attributes Overall score Software License
Strict matching Accuracy
Pre. Rec. F1 Class Tense Aspect
ATT (1) [5] 81.44 80.67 81.05 88.69 73.37 90.68 71.88
KUL (2) [8] 80.69 77.99 79.32 88.46 - - 70.17
ClearTK (4) [6] 81.40 76.38 78.81 86.12 78.20 90.86 67.87 Download BSD-3 Clause
NavyTime (1) [2] 80.73 79.87 80.30 84.03 75.79 91.26 67.48
Temp: (ESAfeature) X, 2013 78.33 61.61 68.97 79.09 - - 54.55
JU_CSE [7] 80.85 76.51 78.62 67.02 74.56 91.76 52.69
FSS-TimeEx [9] 63.13 67.11 65.06 66.00 - - 42.94

Task C: Annotating relations given gold entities

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

Task ABC: Temporal awareness evaluation

Clinical TempEval 2015

  • Clinical TempEval 2015, Clinical TempEval, 2015: web page

Performance measures

Results

Tables show the best result for each system. Lower scoring runs for the same system are not shown.

Time expressions

System name (best run) Short description Main publication Span Class Software License
P R F1 P R F1 A
Baseline Memorize [10] 0.743 0.372 0.496 0.723 0.362 0.483 0.974 - -
KPSCMI: run 1 Rule-based - 0.272 0.782 0.404 0.223 0.642 0.331 0.819 - -
KPSCMI: run 3 Supervised machine learning - 0.693 0.706 0.699 0.657 0.669 0.663 0.948 - -
UFPRSheffield-SVM: run 2 Supervised machine learning [11] 0.741 0.655 0.695 0.723 0.640 0.679 0.977 - -
UFPRSheffield-Hynx: run 5 Rule-based [11] 0.411 0.795 0.542 0.391 0.756 0.516 0.952 - -
BluLab: run 1-3 Supervised machine learning [12] 0.797 0.664 0.725 0.778 0.652 0.709 0.978 - -

Event expressions

System name (best run) Short description Main publication Span Modality Degree Polarity Type Software License
P R F1 P R F1 A P R F1 A P R F1 A P R F1 A
Baseline Memorize [10] 0.876 0.810 0.842 0.810 0.749 0.778 0.924 0.871 0.806 0.838 0.995 0.800 0.740 0.769 0.913 0.846 0.783 0.813 0.966 - -
BluLab: run 1-3 Supervised machine learning [12] 0.887 0.864 0.875 0.834 0.813 0.824 0.942 0.882 0.859 0.870 0.994 0.868 0.846 0.857 0.979 0.834 0.812 0.823 0.941 - -

Temporal relations

Phase 1: text only

System name (best run) Short description Main publication To Document Time Narrative Containers Software License
P R F1 P R F1 P R F1
Baseline Memorize [10] 0.600 0.555 0.577 - - - - - - - -
Baseline TIMEX3 to closest EVENT [10] - - - 0.368 0.061 0.104 0.400 0.061 0.106 - -
BluLab: run 2 Supervised machine learning [12] 0.712 0.693 0.702 0.080 0.142 0.102 0.094 0.179 0.123 - -

Phase 2: manual EVENTs and TIMEX3s

System name (best run) Short description Main publication To Document Time Narrative Containers Software License
P R F1 P R F1 P R F1
Baseline Memorize [10] - - 0.608 - - - - - - - -
Baseline TIMEX3 to closest EVENT [10] - - - 0.514 0.170 0.255 0.554 0.170 0.260 - -
BluLab: run 2 Supervised machine learning [12] - - 0.791 0.109 0.210 0.143 0.140 0.254 0.181 - -

References

  1. ↑ 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.
  2. ↑ 2.0 2.1 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.
  3. ↑ 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.
  4. ↑ 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.
  5. ↑ 5.0 5.1 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.
  6. ↑ 6.0 6.1 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.
  7. ↑ 7.0 7.1 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.
  8. ↑ 8.0 8.1 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.
  9. ↑ 9.0 9.1 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.
  10. ↑ 10.0 10.1 10.2 10.3 10.4 10.5 Steven Bethard, Leon Derczynski, Guergana Savova, James Pustejovsky and Marc Verhagen. SemEval-2015 Task 6: Clinical TempEval. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), (Denver, Colorado, June 2015), Association for Computational Linguistics, pp. 806-814.
  11. ↑ 11.0 11.1 Hegler Tissot, Genevieve Gorrell, Angus Roberts, Leon Derczynski and Marcos Didonet Del Fabro. UFPRSheffield: Contrasting Rule-based and Support Vector Machine Approaches to Time Expression Identification in Clinical TempEval. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), (Denver, Colorado, June 2015), Association for Computational Linguistics, pp. 835-839.
  12. ↑ 12.0 12.1 12.2 12.3 Sumithra Velupillai, Danielle L Mowery, Samir Abdelrahman, Lee Christensen and Wendy Chapman. BluLab: Temporal Information Extraction for the 2015 Clinical TempEval Challenge. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), (Denver, Colorado, June 2015), Association for Computational Linguistics, pp. 815-819.

Unsorted

  • 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.
  • 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

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