Abstract
This paper introduces the novel problem of ‘claim-based queries’ and how digital libraries can be enabled to solve it. Claim-based queries need the identification of a key aspect of research papers: claims. Today, claims are hidden in its unstructured, free text representation within research documents. In this work, a claim is a sentence that constitutes the main contribution of a paper and expresses an association between entities of particular interest in a given domain. In the following, we investigate how to identify claims for subsequent extraction in an unsupervised fashion by a novel integration of neural word embedding representations of claims with a graph based algorithm. For evaluation purposes, we focus on the medical domain: all experiments are based on a real-world corpus from PubMed, where both, limitations and success of our solution can realistically be assessed.
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References
Petasis, G., Karkaletsis, V.: Identifying argument components through TextRank. In: ACL. pp. 94–102 (2016)
González Pinto, J.M., Balke, W.-T.: Can plausibility help to support high quality content in digital libraries? In: TPDL 2017 – 21st International Conference on Theory and Practice of Digital Libraries., Thessaloniki, Greece (2017)
Mihalcea, R., Tarau, P.: TextRank: bringing order into texts. In: Proc. EMNLP, vol. 85, pp. 404–411 (2004)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS, pp. 1–9 (2013)
Mikolov, T., Corrado, G., Chen, K., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of International Conference Learn. Represent (ICLR 2013), pp. 1–12 (2013)
Collobert, R., Weston, J.: A unified architecture for natural language processing. In: Proceedings of the 25th International Conference on Machine Learning - ICML 2008, pp. 160–167 (2008)
Lev, G., Klein, B., Wolf, L.: In defense of word embedding for generic text representation. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds.) NLDB 2015. LNCS, vol. 9103, pp. 35–50. Springer, Cham (2015). doi:10.1007/978-3-319-19581-0_3
Teufel, S.: Argumentative Zoning: Information Extraction from Scientific Text (1999)
Stab, C., Gurevych, I.: Identifying argumentative discourse structures in persuasive essays. In: Proceedings of 2014 Conference on Empirical Methods on Natural Language Processing, pp. 46–56 (2014)
Stab, C., Kirschner, C., Eckle-Kohler, J., Gurevych, I.: Argumentation mining in persuasive essays and scientific articles from the discourse structure perspective. In: CEUR Workshop Proceedings (2014)
Stab, C., Gurevych, I.: Annotating argument components and relations in persuasive essays. In: Proceedings of COLING 2014, 25th International Conference on Computational Linguistics: Technical Papers, pp. 1501–1510 (2014)
Levy, R., Bilu, Y., Hershcovich, D., Aharoni, E., Slonim, N.: Context dependent claim detection. In: International Conference on Computational Linguistics, pp. 1489–1500 (2014)
Lippi, M., Torroni, P.: Context-independent claim detection for argument mining. In: IJCAI International Joint Conference on Artificial Intelligence, pp. 185–191 (2015)
Carstens, L., Toni, F.: Towards relation based argumentation mining. In: Proceedings of the 2nd Workshop on Argumentation Mining, pp. 29–34 (2015)
Habernal, I., Eckle-Kohler, J., Gurevych, I.: Argumentation mining on the web from information seeking perspective. In: Proceedings of the Workshop on Frontiers and Connections between Argumentation Theory and Natural Language Processing, pp. 26–39 (2014)
Rinott, R., Dankin, L., Alzate, C., Khapra, M.M., Aharoni, E., Slonim, N.: Show me your evidence – an automatic method for context dependent evidence detection. In: EMNLP, pp. 440–450 (2015)
Ciccarese, P., Wu, E., Wong, G., Ocana, M., Kinoshita, J., Ruttenberg, A., Clark, T.: The SWAN biomedical discourse ontology. J. Biomed. Inform. 41, 739–751 (2008)
Leskovec, J., Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, Cambridge (2014)
Li, J., Luong, M.-T., Jurafsky, D.: A Hierarchical Neural Autoencoder for Paragraphs and Documents, pp. 1106–1115 (2015)
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González Pinto, J.M., Balke, WT. (2017). Offering Answers for Claim-Based Queries: A New Challenge for Digital Libraries. In: Choemprayong, S., Crestani, F., Cunningham, S. (eds) Digital Libraries: Data, Information, and Knowledge for Digital Lives. ICADL 2017. Lecture Notes in Computer Science(), vol 10647. Springer, Cham. https://doi.org/10.1007/978-3-319-70232-2_1
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DOI: https://doi.org/10.1007/978-3-319-70232-2_1
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