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SemEval-2010 (FrameNet) Edit

SemEval-2010 (Ruppenhofer et al., 2010)[1] (Rel. = Relative, Abs. = Absolute, Over. = overlap measured by Dice coefficient)

SRL task NI recog. DNI vs. INI DNI Linking Notes System ref Performance ref
P R F1 Rel. Abs. P R F1 Over.
Shalmaneser 63.32 38.84 48.12 - - - - - - - Ruppenhofer et al. (2010)[1]
SEMAFOR 1.0 65.28 46.74 54.48 63.4 54.7 35 - - 1.40 - Chen et al. (2010)[2] Ruppenhofer et al. (2010)[1], Silberer & Frank (2012)[3]
CLR 67.02 11.21 19.21 - - - - - - - Ruppenhofer et al. (2010)[1]
GETARUNS++
/VENSES
- - - - 8.0 64.2 5 - - 1.21 - Tonelli & Delmonte (2010)[4] Ruppenhofer et al. (2010)[1], Silberer & Frank (2012)[3]
Silberer & Frank - - - 58 68 40 6.0 8.9 7.1 - model M0 Silberer & Frank (2012)[3] Silberer & Frank (2012)[3]
Laparra & Rigau (2012) - - - - - - 15 25 19 54 Laparra & Rigau (2012)[5] Laparra & Rigau (2012)[5]
Laparra & Rigau (2013) - - - - - - 14 18 16 89 Table 4 Laparra & Rigau (2013)[6] Laparra & Rigau (2013)[6]
Roth & Frank (2015) - - - - - - 21 8 12 - Table 6 (p. 650) Roth & Frank (2015)[7] Roth & Frank (2015)[7]

SemEval-2010 (PropBank) Edit

DNI Linking Notes System ref Performance ref
P R F1 Over.
Feizabadi & Pado 10 20 13 - in domain Feizabadi & Pado (2015)[8] Feizabadi & Pado (2015)[8]
13 30 18 - SEMEVAL train + BNB
Laparra and Rigau (2013) 12 16 14 - - Laparra and Rigau (2013)[6] Feizabadi & Pado (2015)[8]

NomBank Edit

Dataset reference: Gerber and Chai (2010)[9]

Method DNI Linking System ref Perform. ref
P R F1
Gerber & Chai 44.5 40.4 42.3 Gerber and Chai (2010)[9]
Laparra & Rigau 47.9 43.8 45.8 Laparra & Rigau (2012)[10]
Prototype vectors 33.5 39.2 36.1 Schenk & Chiarcos (2016)[11]
LSTM 52.6 41.0 46.1 Do et al. (2017)[12]

References Edit

  1. 1.0 1.1 1.2 1.3 1.4 Ruppenhofer, J., Sporleder, C., Morante, R., Baker, C., & Palmer, M. (2010). SemEval-2010 Task 10: Linking Events and Their Participants in Discourse. In Proceedings of the 5th International Workshop on Semantic Evaluation, ACL 2010 (pp. 45–50). Uppsala, Sweden.
  2. Chen, D., Schneider, N., Das, D., & Smith, N. A. (2010). SEMAFOR: Frame argument resolution with log-linear models. In Proceedings of the 5th International Workshop on Semantic Evaluation (pp. 264–267). Uppsala, Sweden: Association for Computational Linguistics.
  3. 3.0 3.1 3.2 3.3 Silberer, C., & Frank, A. (2012). Casting Implicit Role Linking As an Anaphora Resolution Task. In Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the Main Conference and the Shared Task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (pp. 1–10). Stroudsburg, PA, USA: Association for Computational Linguistics.
  4. Tonelli, S., & Delmonte, R. (2010). VENSES++: Adapting a deep semantic processing system to the identification of null instantiations. In Proceedings of the 5th International Workshop on Semantic Evaluations, ACL 2010 (pp. 296–299). Uppsala, Sweden: Association for Computational Linguistics.
  5. 5.0 5.1 Laparra, E., & Rigau, G. (2012). Exploiting Explicit Annotations and Semantic Types for Implicit Argument Resolution. In Semantic Computing (ICSC), 2012 IEEE Sixth International Conference on (pp. 75–78). doi:10.1109/ICSC.2012.47
  6. 6.0 6.1 6.2 Laparra, E., & Rigau, G. (2013). Sources of Evidence for Implicit Argument Resolution. In Proceedings of the 10th International Conference on Computational Semantics (IWCS2013). Potsdam, Germany.
  7. 7.0 7.1 Roth, M., & Frank, A. (2015). Inducing Implicit Arguments from Comparable Texts: A Framework and its Applications. Computational Linguistics, 41(4), 625–664.
  8. 8.0 8.1 8.2 Feizabadi, P. S., & Pado, S. (2015). Combining Seemingly Incompatible Corpora for Implicit Semantic Role Labeling. Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics (* SEM 2015 ), 40–50.
  9. 9.0 9.1 Matthew Gerber and Joyce Chai. 2010. Beyond Nom- Bank: A Study of Implicit Arguments for Nominal Predicates. In Proceedings of the 48th Annual Meet- ing of the Association for Computational Linguistics (ACL-2010), pages 1583–1592, Uppsala, Sweden.
  10. Egoitz Laparra and German Rigau. 2012. Exploiting Explicit Annotations and Semantic Types for Implicit Argument Resolution. In Proceedings of the 6th Inter- national Conference on Semantic Computing (ICSC- 2012), pages 75–78.
  11. Schenk, N., & Chiarcos, C. (2016). Unsupervised Learning of Prototypical Fillers for Implicit Semantic Role Labeling. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 1473–1479). San Diego, California: Association for Computational Linguistics.
  12. Do, Q. N. T., Bethard, S., & Moens, M.-F. (2017). Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments.