Abstract
We consider the novel problem of learning to rank claim-evidence pairs to ease the task of scientific argumentation. Researchers face daily scientific argumentation when writing research papers or project proposals. Once confronted with a sentence that requires a citation, they struggle to find the manuscript that can support it. In this work, we call such sentences claims – a natural language sentence – that needs a citation to be credible. Evidence in our work refers to a paper that provides credibility to its corresponding claim. We tackle the scientific domain where the task of matching claim-evidence pairs is hindered by complex terminology variations to express the same concept and also by the unknown characteristics beyond content that makes a paper worth to be cited. The former calls for a suitable representation capable of dealing with the challenge of content-based matching considering domain knowledge, whereas the latter implies a need to propose semantic features of suitable characteristics to guide the learning task. To this end, we test the scope and limitation of a deep learning model tailored to the task. Our experiments reveal what specific attributes can guide the learning task, the impact of using domain knowledge in the form of concepts and also the assessment of which metadata of a document, e.g., ‘background’, ‘conclusion’, ‘method’, ‘objective’, or ‘results’ should be considered to achieve better results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aharoni, E., et al.: A benchmark dataset for automatic detection of claims and evidence. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics, pp. 1489–1500. Dublin City University and Association for Computational Linguistics, Dublin (2014)
Bergstra, J., et al.: Algorithms for hyper-parameter optimization. In: Proceedings of the 24th International Conference on Neural Information Processing Systems, pp. 2546–2554. Curran Associates Inc., Lake Tahoe (2011)
Bergstrom, C.T.: Eigenfactor: measuring the value and prestige of scholarly journals. Coll. Res. Libr. News. 68(5), 314–316 (2007)
Bordes, A., Weston, J., Usunier, N.: Open question answering with weakly supervised embedding models. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8724, pp. 165–180. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44848-9_11
Dehghani, M., et al.: Neural ranking models with weak supervision. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 65–74. ACM, New York (2017)
Falagas, M.E., et al.: Comparison of SCImago journal rank indicator with journal impact factor. FASEB J. 22(8), 2623–2628 (2008)
Fetahu, B., et al.: Finding news citations for Wikipedia. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 337–346. ACM, Indianapolis (2016)
Garfield, E.: The history and meaning of the journal impact factor. J. Am. Med. Assoc. 295(1), 90–93 (2006)
Guo, J., et al.: A deep relevance matching model for ad-hoc retrieval. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 55–64. ACM, Indianapolis (2016)
Kalchbrenner, N., et al.: A convolutional neural network for modelling sentences. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 655–665. Association for Computational Linguistics, Baltimore (2014)
Kilicoglu, H., et al.: SemMedDB: a PubMed-scale repository of biomedical semantic predications. J. Bioinform. 28(23), 3158–3160 (2012)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751. Association for Computational Linguistics, Doha (2014)
Levy, R., et al.: Context dependent claim detection. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics, pp. 1489–1500. Dublin City University and Association for Computational Linguistics, Dublin (2014)
Liu, T.-Y.: Learning to rank for information retrieval. Found. Trends Inf. Retr. 3(3), 225–331 (2009)
Lu, Z., Li, H.: A deep architecture for matching short texts. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, vol. 1, pp. 1367–1375. Curran Associates Inc., Lake Tahoe (2013)
Mikolov, T., et al.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, pp. 3111–3119. Curran Associates Inc., Lake Tahoe (2013)
Mitra, B., Craswell, N.: An introduction to neural information retrieval. Found. Trends Inf. Retr. 13(1), 1–126 (2018)
Rindflesch, T.C., Fiszman, M.: The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text. J. Biomed. Inform. 36, 462–477 (2003)
Roitman, H., et al.: On the retrieval of Wikipedia articles containing claims on controversial topics. In: Proceedings of the 25th International Conference Companion on World Wide Web, pp. 991–996 International World Wide Web Conferences Steering Committee, Montreal (2016)
Severyn, A., Moschitti, A.: Learning to rank short text pairs with convolutional deep neural networks. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 373–382. ACM, Santiago (2015)
Shen, Y., et al.: Learning semantic representations using convolutional neural networks for web search. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 373–374. ACM, New York (2014)
De Vine, L., et al.: Medical semantic similarity with a neural language model. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management, pp. 1819–1822. ACM, New York (2014)
Zamani, H., et al.: From neural re-ranking to neural ranking: learning a sparse representation for inverted indexing. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 497–506. ACM, Torino (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
González Pinto, J.M., Celik, S., Balke, WT. (2019). Learning to Rank Claim-Evidence Pairs to Assist Scientific-Based Argumentation. In: Doucet, A., Isaac, A., Golub, K., Aalberg, T., Jatowt, A. (eds) Digital Libraries for Open Knowledge. TPDL 2019. Lecture Notes in Computer Science(), vol 11799. Springer, Cham. https://doi.org/10.1007/978-3-030-30760-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-30760-8_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30759-2
Online ISBN: 978-3-030-30760-8
eBook Packages: Computer ScienceComputer Science (R0)