Abstract
Negation is both an operation in formal logic and in natural language by which a proposition is replaced by one stating the opposite, as by the addition of “not” or another negation cue. Treating negation in an adequate way is required for cognitive reasoning, which aims at modeling the human ability to draw meaningful conclusions despite incomplete and inconsistent knowledge. One task of cognitive reasoning is answering questions given by sentences in natural language. There are tools based on discourse representation theory to convert sentences automatically into a formal logic representation, and additional knowledge can be added using the predicate names in the formula and knowledge databases. However, the knowledge in logic databases in practice always is incomplete. Hence, forward reasoning of automated reasoning systems alone does not suffice to derive answers to questions because, instead of complete proofs, often only partial positive knowledge can be derived, while negative knowledge is used only during the reasoning process. In consequence, we aim at eliminating syntactic negation, strictly speaking, the negated event or property. In this paper, we describe an effective procedure to determine the negated event or property in order to replace it by its inverse. This lays the basis of cognitive reasoning, employing both logic and machine learning for general question answering. We evaluate our procedure by several benchmarks and demonstrate its practical usefulness in our cognitive reasoning system.
The authors gratefully acknowledge the support of the German Research Foundation (DFG) under the grants SCHO 1789/1-1 and STO 421/8-1 CoRg – Cognitive Reasoning.
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Notes
- 1.
All used benchmark sets, our implementation in Python, and an extended version of this paper are available at http://arxiv.org/abs/2012.12641.
References
Agirre, E., Bos, J., Diab, M., Manandhar, S., Marton, Y., Yuret, D. (eds.): *SEM 2012: 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 (SemEval 2012). Association for Computational Linguistics, Montréal, Canada (2012). http://www.aclweb.org/anthology/volumes/S12-1/
Álvez, J., Lucio, P., Rigau, G.: Adimen-SUMO: reengineering an ontology for first-order reasoning. Int. J. Semant. Web Inf. Syst. 8(4), 80–116 (2012). http://doi.org/10.4018/jswis.2012100105
Antoniou, G.: A tutorial on default logics. ACM Comput. Surv. 31(4), 337–359 (1999). http://doi.org/10.1145/344588.344602
Basile, V., Bos, J., Evang, K., Venhuizen, N.: UGroningen: negation detection with discourse representation structures. In: Agirre et al. [1], pp. 301–309. http://www.aclweb.org/anthology/S12-1040
Basile, V., Cabrio, E., Schon, C.: KNEWS: using logical and lexical semantics to extract knowledge from natural language. In: Proceedings of the European Conference on Artificial Intelligence (ECAI) (2016). http://hal.inria.fr/hal-01389390
Baumgartner, P., Furbach, U., Pelzer, B.: The hyper tableaux calculus with equality and an application to finite model computation. J. Log. Comput. 20(1), 77–109 (2010). http://doi.org/10.1093/logcom/exn061
Bender, M., Pelzer, B., Schon, C.: System description: E-KRHyper 1.4. In: Bonacina, M.P. (ed.) CADE 2013. LNCS (LNAI), vol. 7898, pp. 126–134. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38574-2_8
Bos, J.: Wide-coverage semantic analysis with Boxer. In: Semantics in Text Processing. STEP 2008 Conference Proceedings, pp. 277–286. College Publications (2008). http://www.aclweb.org/anthology/W08-2222
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics (2019). http://aclweb.org/anthology/papers/N/N19/N19-1423/
Furbach, U., Hölldobler, S., Ragni, M., Schon, C., Stolzenburg, F.: Cognitive reasoning: a personal view. KI 33(3), 209–217 (2019). http://link.springer.com/article/10.1007/s13218-019-00603-3
Habernal, I., Wachsmuth, H., Gurevych, I., Stein, B.: SemEval-2018 task 12: the argument reasoning comprehension task. In: Proceedings of The 12th International Workshop on Semantic Evaluation. pp. 763–772. Association for Computational Linguistics, New Orleans, Louisiana (June 2018). http://www.aclweb.org/anthology/S18-1121
Horn, L.R., Wansing, H.: Negation. In: Zalta, E.N. (ed.): Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University (2020). http://plato.stanford.edu/entries/negation/
Janssen, T.M.V., Zimmermann, T.E.: Montague semantics. In: Zalta, E.N. (ed.): Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University (2021). http://plato.stanford.edu/archives/sum2021/entries/montague-semantics/
Jiménez-Zafra, S.M., Morante, R., Teresa Martín-Valdivia, M., Ureña-López, L.A.: Corpora annotated with negation: an overview. Comput. Linguist. 46(1), 1–52 (2020). http://doi.org/10.1162/coli_a_00371
Jurafsky, D., H. James, M.: Vector semantics and embeddings. In: Speech and Language Processing: an Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, chap. 6, pp. 96–126. Prentice Hall, Upper Saddle River, N.J., 3rd edn. (2020). http://web.stanford.edu/~jurafsky/slp3/ed3book.pdf. Draft
Kamp, H., Reyle, U.: From discourse to logic: an introduction to modeltheoretic semantics of natural language, formal logic and discourse representation theory. Springer, Dordrecht (1993). http://www.springer.com/de/book/9780792310280
Kolhatkar, V., Wu, H., Cavasso, L., Francis, E., Shukla, K., Taboada, M.: The SFU opinion and comments corpus: a corpus for the analysis of online news comments. Corpus Pragmat. 4, 155–190 (2020). http://link.springer.com/article/10.1007/s41701-019-00065-w
Liu, H., Singh, P.: ConceptNet - a practical commonsense reasoning tool-kit. BT Technol. J. 22(4), 211–226 (2004). http://doi.org/10.1023/B:BTTJ.0000047600.45421.6d
Lyons, J.: Semantics, vol. 2. Cambridge University Press, Cambridge, New York, Melbourne, Madrid (1977). http://doi.org/10.1017/CBO9780511620614
Maslan, N., Roemmele, M., Gordon, A.S.: One hundred challenge problems for logical formalizations of commonsense psychology. In: Twelfth International Symposium on Logical Formalizations of Commonsense Reasoning, Stanford, CA (2015). http://www.aaai.org/ocs/index.php/SSS/SSS15/paper/viewFile/10252/10080
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995). http://doi.org/10.1145/219717.219748
Morante, R., Blanco, E.: *SEM 2012 shared task: resolving the scope and focus of negation. In: Agirre et al. [1], pp. 265–274. http://www.aclweb.org/anthology/S12-1035
Morante, R., Daelemans, W.: ConanDoyle-neg: annotation of negation cues and their scope in Conan Doyle stories. In: Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12). pp. 1563–1568. European Language Resources Association (ELRA), Istanbul, Turkey (2012). http://www.lrec-conf.org/proceedings/lrec2012/pdf/221_Paper.pdf
Mostafazadeh, N., Roth, M., Louis, A., Chambers, N., Allen, J.: LSDSem 2017 shared task: the story cloze test. In: Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics, pp. 46–51 (2017). http://doi.org/10.18653/v1/w17-0906
Mueller, E.T.: Commonsense Reasoning. Morgan Kaufmann, San Francisco, 2nd edn. (2014). http://dl.acm.org/doi/book/10.5555/2821577
Niven, T., Kao, H.Y.: Probing neural network comprehension of natural language arguments. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4658–4664. Association for Computational Linguistics, Florence, Italy (July 2019). http://www.aclweb.org/anthology/P19-1459
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training. Technical report, Open AI (2018). http://openai.com/blog/language-unsupervised/
Schon, C., Siebert, S., Stolzenburg, F.: The CoRg project: cognitive reasoning. KI 33(3), 293–299 (2019). http://link.springer.com/article/10.1007/s13218-019-00601-5
Siebert, S., Schon, C., Stolzenburg, F.: Commonsense reasoning using theorem proving and machine learning. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2019. LNCS, vol. 11713, pp. 395–413. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29726-8_25
Speer, R., Chin, J., Havasi, C.: ConceptNet 5.5: an open multilingual graph of general knowledge. In: AAAI Conference on Artificial Intelligence, pp. 4444–4451 (2017). http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14972
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Schon, C., Siebert, S., Stolzenburg, F. (2021). Negation in Cognitive Reasoning. In: Edelkamp, S., Möller, R., Rueckert, E. (eds) KI 2021: Advances in Artificial Intelligence. KI 2021. Lecture Notes in Computer Science(), vol 12873. Springer, Cham. https://doi.org/10.1007/978-3-030-87626-5_16
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