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Answer Set Programming in Linguistics

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Abstract

This survey collects scientific works where answer set programming, a declarative knowledge representation and reasoning formalism, is applied to natural language processing and computational linguistics.

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Notes

  1. http://nl2kr.engineering.asu.edu/.

References

  1. Bailey D, Harrison A, Lierler Y, Lifschitz V, Michael J (2015) The winograd schema challenge and reasoning about correlation. In: Working notes of the symposium on logical formalizations of commonsense reasoning (CommonSense), pp 17–24

  2. Baker CF, Fillmore CJ, Lowe JB (1998) The berkeley framenet project. In: Proceedings of the 17th international conference on computational linguistics (COLING). Association for Computational Linguistics, pp 86–90

  3. Balduccini M, Baral C, Lierler Y (2008) Knowledge representation and question answering. Handbook of knowledge representation. Elsevier, New York, pp 779–819

    Chapter  Google Scholar 

  4. Baral C, Dzifcak J, Gonzalez MA, Gottesman A (2012) Typed answer set programming lambda calculus theories and correctness of inverse lambda algorithms with respect to them. Theory Pract Logic Programm 12(4–5):775–791

    Article  MathSciNet  MATH  Google Scholar 

  5. Baral C, Dzifcak J, Gonzalez MA, Zhou J (2011) Using inverse lambda and generalization to translate english to formal languages. In: Proceedings of the ninth international conference on computational semantics (IWCS). Association for Computational Linguistics, pp 35–44

  6. Baral C, Liang S (2012) From knowledge represented in frame-based languages to declarative representation and reasoning via ASP. In: Proceedings of the thirteenth international conference on the principles of knowledge representation and reasoning (KR). AAAI Press, pp 413–423

  7. Baral C, Tari L (2006) Using ansprolog with link grammar and WordNet for QA with deep reasoning. In: Ninth international conference on information technology (ICIT). IEEE, pp 125–128

  8. Baumgartner P, Burchardt A (2004) Logic programming infrastructure for inferences on FrameNet. In: Alferes JJ, Leite J (eds) Logics in artificial intelligence. Springer, Berlin, Heidelberg, pp 591–603

    Chapter  Google Scholar 

  9. Bos J (2008) Wide-coverage semantic analysis with Boxer. In: Conference on Semantics in Text Processing. Association for Computational Linguistics, pp 277–286

  10. Brooks D, Erdem E, Erdoǧan S, Minett J, Ringe D (2007) Inferring phylogenetic trees using answer set programming. J Autom Reason 39:471–511

    Article  MathSciNet  MATH  Google Scholar 

  11. Chaudhri VK, Cheng BH, Overholtzer A, Roschelle J, Spaulding A, Clark P, Greaves M, Gunning D (2013) Inquire biology: a textbook that answers questions. AI Magazine 34(3):55–72

    Article  Google Scholar 

  12. Chaudhri VK, Heymans S, Wessel M, Son TC (2013) Object-oriented knowledge bases in logic programming. In: Technical communication of international conference in logic programming

  13. Chaudhri VK, Son TC (2012) Specifying and reasoning with underspecified knowledge bases using answer set programming. In:Proceedings of the thirteenth international conference on principles of knowledge representation and reasoning (KR). AAAI Press, pp 424–434

  14. Dagan I, Glickman O, Magnini B (2006) The PASCAL recognising textual entailment challenge. In: Machine Learning Challenges. Springer, New York, pp 177–190

  15. Drescher C, Walsh T (2011) Modelling grammar constraints with answer set programming. In: Gallagher JP, Gelfond M (eds) Technical communication of international conference in logic programming, vol 11. Schloss Dagstuhl-Leibniz-Zentrum für Informatik, Germany, pp 28–39

    Google Scholar 

  16. Erdem E, Lifschitz V, Ringe D (2006) Temporal phylogenetic networks and logic programming. Theory Pract Logic Programm 6(5):539–558

    Article  MathSciNet  MATH  Google Scholar 

  17. Erdem E, Oztok U (2015) Generating explanations for biomedical queries. Theory Pract Logic Programm 15(1):35–78

    Article  MathSciNet  MATH  Google Scholar 

  18. Fuchs NE, Kaljurand K, Kuhn T (2008) Attempto controlled english for knowledge representation. In: Baroglio C, Bonatti PA, Małuszyński J, Marchiori M, Polleres A, Schaffert S (eds) Reasoning web. Springer, Berlin, Heidelberg, pp 104–124

    Chapter  Google Scholar 

  19. Guy SC, Schwitter R (2017) The PENG ASP system: architecture, language and authoring tool. Lang Resour Eval 51(1):67–92

    Article  Google Scholar 

  20. Hobbs J, Stickel M, Martin P, Edwards D (1993) Interpretation as abduction. Artif Intel 63(1–2):69–142

    Article  Google Scholar 

  21. Hopcroft JE, Motwani R, Ullman JD (2000) Introduction to automata theory, languages, and computation. Pearson, London

    MATH  Google Scholar 

  22. Horn LR, Ward G (2006) The handbook of pragmatics. Blackwell, Oxford

    Book  Google Scholar 

  23. Inclezan D (2013) An application of ASP to the field of second language acquisition. In: Cabalar P, Son TC (eds) Logic programming and nonmonotonic rteasoning (LPNMR). Springer, Berlin, Heidelberg, pp 395–400

    Chapter  Google Scholar 

  24. Kamp H, Reyle U (1993) From discourse to logic: introduction to model theoretic semantics of natural language, formal logic and discourse representation theory. Studies in linguistics and philosophy. Kluwer, Dordrecht

    Google Scholar 

  25. Kazmi M, Schüller P (2016) Inspire at SemEval-2016 Task 2: interpretable semantic textual similarity alignment based on answer set programming. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval). Association for Computational Linguistics, pp 1109–1115

  26. Kazmi M, Schüller P, Saygın Y (2017) Improving Scalability of Inductive Logic Programming via Pruning and Best-Effort Optimisation. Expert Syst Appl 87:291–303

    Article  Google Scholar 

  27. Khandelwal P, Zhang S, Sinapov J, Leonetti M, Thomason J, Yang F, Gori I, Svetlik M, Khante P, Lifschitz V, Aggarwal J, Mooney R, Stone P (2017) BWIBots: A platform for bridging the gap between AI and human-robot interaction research. Int J Rob Res 36(5–7):635–659

    Article  Google Scholar 

  28. Kracht M (2003) The mathematics of language. Studies in generative grammar, vol 63. Walter De Gruyter, Berlin

    MATH  Google Scholar 

  29. Levesque HJ, Davis E, Morgenstern L (2012) The Winograd schema challenge. In: Proceedings of the thirteenth international conference on principles of knowledge representation and reasoning (KR). AAAI Press, pp 552–561

  30. Lierler Y, Lifschitz V (2013) Logic programs vs. first-order formulas in textual inference. In:: Proceedings of the 10th international conference on computational semantics (IWCS), short papers. Association for Computational Linguistics, pp 340–346

  31. Lierler Y, Schüller P (2012) Parsing combinatory categorial grammar via planning in answer set programming. In: Erdem E, Lee J, Lierler Y, Pearce D (eds) Correct reasoning. Springer, Berlin, Heidelberg, pp 436–453

    Chapter  Google Scholar 

  32. Liu Q, Gao Z, Liu B, Zhang Y (2013) A logic programming approach to aspect extraction in opinion mining. In: IEEE/WIC/ACM international joint conferences on web intelligence (WI) and intelligent agent technologies (IAT). IEEE, pp 276–283

  33. Martin JH, Jurafsky D (2000) Speech and language processing. Pearson, London

    Google Scholar 

  34. Miller GA (1995) WordNet: a lexical database for English. Commun ACM 38(11):39–41

    Article  Google Scholar 

  35. Mitra A, Baral C (2016) Addressing a question answering challenge by combining statistical methods with inductive rule learning and reasoning. In: AAAI, pp 2779–2785

  36. Muggleton S, De Raedt L, Poole D, Bratko I, Flach P, Inoue K, Srinivasan A (2012) ILP turns 20: Biography and future challenges. Mach Learn 86(1):3–23

    Article  MathSciNet  MATH  Google Scholar 

  37. Muggleton SH, Lin D, Pahlavi N, Tamaddoni-Nezhad A (2014) Meta-interpretive learning: application to grammatical inference. Mach Learn 94(1):25–49

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  39. Schaub T, Woltran S (2018) Answer set programming unleashed! Künstliche Intelligenz (this issue)

  40. Scherl R, Inclezan D, Gelfond M (2010) Automated inference of socio-cultural information from natural language conversations. In: IEEE second international conference on social computing (SocialCom). IEEE, pp 480–487

  41. Schüller P (2013) Flexible combinatory categorial grammar parsing using the CYK algorithm and answer set programming. In: Cabalar P, Son TC (eds) Logic Programming and Nonmonotonic Reasoning (LPNMR). Springer, Berlin, Heidelberg, pp 499–511

    Chapter  Google Scholar 

  42. Schüller P (2014) Tackling Winograd schemas by formalizing relevance theory in knowledge graphs. In: Fourteenth International conference on the principles of knowledge representation and reasoning (KR). AAAI Press, pp 358–367

  43. Schüller P (2016) Modeling variations of first-order horn abduction in answer set programming. Fundamenta Informaticae 149:159–207

    Article  MathSciNet  MATH  Google Scholar 

  44. Schüller P (2018) Adjudication of coreference annotations via Answer Set optimization. J Exp Theor Artif Intell (forthcoming). https://doi.org/10.1080/0952813X.2018.1456793

    MATH  Google Scholar 

  45. Sharma A, Vo NH, Aditya S, Baral C (2015) Towards addressing the Winograd Schema Challenge—building and using a semantic parser and a knowledge hunting module. In: Proceedings of the 24th international joint conference on artificial intelligence (IJCAI). AAAI Press, pp 1319–1325

  46. Sleator D, Temperley D (1993) Parsing english with a link grammar. In: International Workshop on Parsing Technologies. arXiv:cmp-lg/9508004

  47. Steedman M (2000) The syntactic process. MIT Press, Cambridge

    MATH  Google Scholar 

  48. Tari L, Anwar S, Liang S, Cai J, Baral C (2010) Discovering drug–drug interactions: a text-mining and reasoning approach based on properties of drug metabolism. Bioinformatics 26:i547–i553

    Article  Google Scholar 

  49. Todorova Y, Gelfond M (2012) Toward question answering in travel domains. In: Erdem E, Lee J, Lierler Y, Pearce D (eds) Correct reasoning. Springer, Berlin, Heidelberg, pp 311–326

    Chapter  Google Scholar 

  50. Toivanen JM, Järvisalo M, Toivonen H (2013) Harnessing constraint programming for poetry composition. In: Proceedings of the fourth international conference on computational creativity, pp 160–167

  51. Turing A (1950) Computing machinery and intelligence. Mind 59:433–460

    Article  MathSciNet  Google Scholar 

  52. Vo NH, Mitra A, Baral C (2015) The NL2KR platform for building natural language translation systems. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing. Association for Computational Linguistics, pp 899–908

  53. Voorhees EM (1999) The TREC-8 question answering track report. In: Proceedings of the 8th text retrieval conference (TREC), pp 77–82

  54. Weston J, Bordes A, Chopra S, Mikolov T (2015) Towards AI-complete question answering: a set of prerequisite toy tasks. arXiv:1502.05698

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  1. Institute für Logic and Computation, Technische Universität Wien, Favoritenstraße 9–11, 1040, Vienna, Austria

    Peter Schüller

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Schüller, P. Answer Set Programming in Linguistics. Künstl Intell 32, 151–155 (2018). https://doi.org/10.1007/s13218-018-0542-z

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