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Answer Set Programming Applied to Coreference Resolution and Semantic Similarity

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Abstract

We describe two research projects about solving problems in Computational Linguistics using Answer Set Programming, and we conclude with several lessons learned from these projects.

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Notes

  1. https://github.com/knowlp/caspr-coreference-tool.

  2. https://bitbucket.com/knowlp/asp-fo-abduction.

  3. https://github.com/knowlp/XHAIL.

References

  1. Schaub T, Woltran S (2018) Answer set programming unleashed! Künstliche Intelligenz (forthcoming)

  2. Agirre E, Gonzalez-Agirre A, Lopez-Gazpio I, Maritxalar M, Rigau G, Uria L (2016) SemEval-2016 Task 2: Interpretable Semantic Textual Similarity. In: SemEval, pp 512–524

  3. Alviano M, Dodaro C, Faber W, Leone N, Ricca F (2013) WASP: a native ASP solver based on constraint learning. In: LPNMR, pp 54–66

  4. Alviano M, Dodaro C, Marques-Silva J, Ricca F (2015) Optimum stable model search: algorithms and implementation. J Logic Comput, exv061

  5. Banjade R, Niraula NB, Maharjan N, Rus V, Stefanescu D, Lintean M, Gautam D (2015) NeRoSim: a system for measuring and interpreting semantic textual similarity. In: SemEval, pp 164–171

  6. Cuteri B, Dodaro C, Ricca F, Schüller P (2017) Constraints, lazy constraints, or propagators in ASP solving: an empirical analysis. Theor Pract Log Prog 17:780–799

    Article  MathSciNet  MATH  Google Scholar 

  7. Gebser M, Kaminski R, Kaufmann B, Ostrowski M, Schaub T, Wanko P (2016) Theory solving made easy with Clingo 5. In: ICLP TC. OASIcs, vol 52, pp 2:1–2:15

  8. Hobbs JR, Stickel M, Martin P, Edwards D (1993) Interpretation as abduction. Artif Intell 63(1–2):69–142

    Article  Google Scholar 

  9. Kazmi M, Schüller P (2016) Inspire at SemEval-2016 Task 2: interpretable semantic textual similarity alignment based on Answer Set Programming. In: SemEval, pp 1109–1115

  10. Kazmi M, Schüller P, Saygin Y (2017) Improving scalability of inductive logic programming via pruning and best-effort optimisation. Expert Syst Appl 87:291–303

    Article  Google Scholar 

  11. Lee H, Chang A, Peirsman Y, Chambers N, Surdeanu M, Jurafsky D (2013) Deterministic coreference resolution based on entity-centric. Precision-Ranked Rules. Comput Linguist 39(4):885–916

    Article  Google Scholar 

  12. Lierler Y, Schüller P (2013) Towards a tight integration of syntactic parsing with semantic disambiguation by means of declarative programming. In: IWCS, pp 383–389

  13. Müller C, Strube M (2006) Multi-level annotation of linguistic data with MMAX2. Corpus technology and language pedagogy: new resources, new tools, new methods. Peter Lang, Bern, pp 197–214

    Google Scholar 

  14. Ray O (2009) Nonmonotonic abductive inductive learning. J Appl Logic 7:329–340

    Article  MathSciNet  MATH  Google Scholar 

  15. Schüller P (2016) Modeling variations of first-order horn abduction in answer set programming. Fundam Inform 149:159–207

    Article  MathSciNet  MATH  Google Scholar 

  16. Schüller P (2018) Adjudication of coreference annotations via answer set optimization. J Exp Theor Artif Intell (forthcoming)

  17. Schüller P, Cingilli K, Tunçer F, Sürmeli BG, Pekel A, Karatay AH, Karakas HE (2017) Marmara Turkish coreference corpus and coreference resolution baseline. arXiv:1706.01863

  18. Versley Y, Ponzetto SP, Poesio M, Eidelman V, Jern A, Smith J, Yang X, Moschitti A (2008) BART: a modular toolkit for coreference resolution. In: ACL, pp 9–12

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Acknowledgements

I am grateful to my collaborators and to my students. OmSieve and Inspire have been supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grants 114E430 and 114E777.

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Correspondence to Peter Schüller.

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Schüller, P. Answer Set Programming Applied to Coreference Resolution and Semantic Similarity. Künstl Intell 32, 207–208 (2018). https://doi.org/10.1007/s13218-018-0539-7

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