From Discourse Representation Structure to Event Semantics: A Simple Conversion?
Daniel Dakota, Sandra Kübler
Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 343–352 (2016)
Abstract. Many applications in Natural Language Processing require a semantic analysis of sentences in terms of truth-conditional representations, often with specific desiderata in terms of which information needs to be included in the semantic analysis. However, there are only very few tools that allow such an analysis. We investigate the representations of an automatic analysis pipeline of the C\&C parser and Boxer to determine whether Boxer's analyses in form of Discourse Representation Structure can be successfully converted into a more surface oriented event semantic representation, which will serve as input for a fusion algorithm for fusing hard and soft information. We use a data set on synthetic counter intelligence messages for our investigation. We provide a basic pipeline for conversion and subsequently discuss areas in which ambiguities and differences bertween the semantic representations present challenges in the conversion process.
- X. Carreras and L. Màrquez, “Introduction to the conll-2004 shared task: Semantic role labeling,” in Proceedings of the Eighth Conference on Computational Natural Language Learning (CoNLL-04), Boston, MA, 2004, pp. 89–97.
- X. Carreras and L. Màrquez, “Introduction to the CoNLL-2005 shared task: Semantic role labeling,” in Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-05), Ann Arbor, MI, 2005, pp. 152–164.
- L. Màrquez, X. Carreras, K. Litkowski, and S. Stevenson, “Semantic role labeling: An introduction to the special issue,” Computational Linguistics, vol. 34, no. 2, pp. 145–159, 2008.
- T. Wickramarathne, K. Premaratne, M. Murthi, M. Scheutz, S. Kübler, and M. Pravia, “Belief theoretic methods for soft and hard data fusion,” in Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, 2011.
- R. Nunez, T. Wickramarathne, K. Premaratne, M. Murthi, S. Kübler, M. Scheutz, and M. Pravia, “Credibility assesment and inference for fusion of hard and soft information,” in Proceedings of the International Conference on Cross-Cultural Decision Making (HSBC FOCUS), San Francisco, CA, 2012.
- A. Cahill, M. McCarthy, J. van Genabith, and A. Way, “Quasi-logical forms from F-structures for the Penn Treebank,” in Proceedings of the Fifth International Workshop on Computational Semantics, Tilburg, The Netherlands, 2003.
- C. Gardent and Y. Parmentier, “SemTAG: A platform for specifying Tree Adjoining Grammars and performing TAG-based semantic con- struction,” in Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, Prague, Czech Republic, 2007, pp. 13–16.
- M. Steedman, The Syntactic Process. Cambridge, MA: MIT Press, 2001.
- S. Clark and J. Curran, “Formalism-independent parser evaluation with CCG and DepBank,” in Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics (ACL), Prague, Czech Republic, 2007.
- S. Clark and J. Curran, “Wide-coverage efficient statistical parsing with CCG and log-linear models,” Computational Linguistics, vol. 33, no. 4, pp. 493—552, 2007.
- J. R. Curran, S. Clark, and J. Bos, “Linguistically motivated large-scale nlp with c&c and boxer,” in Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, ser. ACL ’07. Prague, Czech Republic: ACL, 2007, pp. 33–36.
- J. Bos, “Wide-coverage semantic analysis with boxer,” in Semantics in Text Processing. STEP 2008 Conference Proceedings, J. Bos and R. Delmonte, Eds. College Publications, 2008, pp. 277–286.
- J. Bos, “Open-domain semantic parsing with boxer,” in Proceedings of the 20th Nordic Conference of Computational Linguistics (NODALIDA 2015), Vilnius, Lithuania, May 2015, pp. 301–304. [Online]. Available: http://www.let.rug.nl/bos/pubs/Bos2015NoDaLiDa.pdf
- H. Kamp, “A theory of truth and semantic representation,” in Formal Methods in the Study of Language, J. Groenendijk, T. Janssen, and M. Stokhof, Eds. Amsterdam: Mathematical Centre, 1981, pp. 277–322.
- H. Kamp and U. Reyle, From Discourse to Logic: An Introduction to Modeltheoretic Semantic of Natural Language, Formal Logic and DRT. Dordrecht: Kluwer, 1993.
- L. Zettlemoyer and M. Collins, “Learning context-dependent mappings from sentences to logical form,” in Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, Suntec, Singapore, 2009, pp. 976–984.
- Y. Artzi, K. Lee, and L. Zettlemoyer, “Broad-coverage CCG semantic parsing with AMR,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, September 2015, pp. 1699–1710.
- R. Dabarera, R. Nunez, K. Premaratne, and M. Murthi, “Dynamics of belief theoretic agent opinions under bounded confidence,” in International Conference on Information Fusion (FUSION), Salamanca, Spain, 2014.
- R. Nunez, M. Murthi, and K. Premaratne, “Efficient computation of DS-based uncertain logic operations and its application to hard and soft data fusion,” in International Conference on Information Fusion (FUSION), Salamanca, Spain, 2014.
- J. L. Graham, D. L. Hall, and J. Rimland, “A coin-inspired synthetic dataset for qualitative evaluation of hard and soft fusion systems,” in Proceedings of the 14th International Conference on Information Fusion (FUSION). Chicago, Illinois: IEEE, July 2011, pp. 1–8. [Online]. Available: https://www.researchgate.net/profile/David_Hall20/publication/261344900_A_COIN-inspired_synthetic_dataset_for_qualitative_evaluation_of_hard_and_soft_fusion_systems/links/542165450cf203f155c6693c.pdf
- D. Davidson, Inquiries into Truth and Interpretation. Oxford University Press, 1984.
- J. Hockenmaier and M. Steedman, “CCGbank: A corpus of CCG deriva- tions and dependency structures extracted from the Penn Treebank,” Computational Linguistics, vol. 33, no. 3, pp. 355–396, 2007.
- L. Hirschman and N. Chinchor, “MUC-7 coreference task definition,” 1997, message Understanding Conference. [Online]. Available: http://www-nlpir.nist.gov/related_projects/muc/proceedings/co_task.html
- J. R. Curran and S. Clark, “Language independent ner using a maximum entropy tagger,” in Proceedings of CoNLL-03, Edmonton, Canada, 2003, pp. 164–167.
- J. Bos and M. Nissim, “Uncovering noun-noun compound relations by gamification,” in Proceedings of the 20th Nordic Conference of Computational Linguistics (NODALIDA 2015), Vilnius, Lithuania, May 2015, pp. 251–255. [Online]. Available: http://www.let.rug.nl/bos/pubs/BosNissim2015NoDaLiDa.pdf
- J. Bos, “Implementing the binding and accommodation theory for anaphora resolution and presupposition projection,” Computational Linguistics, vol. 29, no. 2, pp. 179–210, 2003.
- S. Pradhan, A. Moschitti, N. Xue, O. Uryupina, and Y. Zhang, “CoNLL-2012 shared task: Modeling multilingual unrestricted coreference in OntoNotes,” in Proceedings of the Sixteenth Conference on Computational Natural Language Learning (CoNLL 2012), Jeju, Korea, 2012.
- S. Pradhan, L. Ramshaw, M. Marcus, M. Palmer, R. Weischedel, and N. Xue, “CoNLL-2011 shared task: Modeling unrestricted coreference in OntoNotes,” in Proceedings of the Fifteenth Conference on Computational Natural Language Learning (CoNLL 2011), Portland, OR, 2011.
- M. Palmer, D. Gildea, and P. Kingsbury, “The proposition bank: An annotated corpus of semantic roles,” Computational Linguistics, vol. 31, no. 1, pp. 71–106, 2005.