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Analysis of COLIEE Information Retrieval Task Data Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Leveraging Document-Specific Information for Classifying Relations in Scientific Articles

Authors : Qin Dai, Naoya Inoue, Paul Reisert, Kentaro Inui

Published in: New Frontiers in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

Tremendous amount of knowledge is present in the ever-growing scientific literature. In order to grasp this massive amount knowledge, various computational tasks are proposed for training computers to read and analyze scientific documents. As one of these task, semantic relationship classification aims at automatically analyzing semantic relationships in scientific documents. Conventionally, only a limited number of commonly used knowledge bases such as Wikipedia are used for collecting background information for this task. In this work, we hypothesize that scientific papers also could be utilized as a source of background information for semantic relationship classification. Based on the hypothesis, we propose the model that is capable of extracting background information from unannotated scientific papers. Preliminary experiments on the RANIS dataset [1] proves the effectiveness of the proposed model on relationship classification in scientific articles.

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previous chapter A Hierarchical Neural Extractive Summarizer for Academic Papers
next chapter Investigating Classroom Activities in English Conversation Lessons Based on Activity Coding and Data Visualization
Footnotes
1
The phrase target entity refers not merely the concept denoted by noun or noun phrase, but it could be an action denoted by a verb or verb phrase and some quality denoted by an adjective, adverb, etc.
 
2
This example is taken from W13-2242, ACL anthology (http://​aclanthology.​info).
 
Literature
1.
Tateisi, Y., Shidahara, Y., Miyao, Y., Aizawa, A.: Annotation of computer science papers for semantic relation extraction. In: LREC, pp. 1423–1429 (2014)
2.
Teufel, S., et al.: Argumentative zoning: information extraction from scientific text. Ph.D. thesis, University of Edinburgh (2000)
3.
Cohen, K.B., Demner-Fushman, D., Ananiadou, S., Tsujii, J.: BioNLP 2017 (2017)
4.
Augenstein, I., Das, M., Riedel, S., Vikraman, L., McCallum, A.: SemEval 2017 Task 10: ScienceIE-extracting keyphrases and relations from scientific publications. arXiv preprint arXiv:​1704.​02853 (2017)
5.
6.
7.
Boschee, E., Weischedel, R., Zamanian, A.: Automatic information extraction. In: Proceedings of the International Conference on Intelligence Analysis, vol. 71. Citeseer (2005)
8.
Suchanek, F.M., Ifrim, G., Weikum, G.: Combining linguistic and statistical analysis to extract relations from web documents. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 712–717. ACM (2006)
9.
Chan, Y.S., Roth, D.: Exploiting background knowledge for relation extraction. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 152–160. Association for Computational Linguistics (2010)
10.
Zeng, D., Liu, K., Lai, S., Zhou, G., Zhao, J., et al.: Relation classification via convolutional deep neural network. In: COLING, pp. 2335–2344 (2014)
11.
12.
dos Santos, C.N., Xiang, B., Zhou, B.: Classifying relations by ranking with convolutional neural networks. arXiv preprint arXiv:​1504.​06580 (2015)
13.
Ammar, W., Peters, M., Bhagavatula, C., Power, R.: The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 592–596 (2017)
14.
Miwa, M., Bansal, M.: End-to-end relation extraction using LSTMs on sequences and tree structures. arXiv preprint arXiv:​1601.​00770 (2016)
15.
GuoDong, Z., Jian, S., Jie, Z., Min, Z.: Exploring various knowledge in relation extraction. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 427–434. Association for Computational Linguistics (2005)
16.
Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)CrossRef
17.
18.
Hahn-Powell, G., Bell, D., Valenzuela-Escárcega, M.A., Surdeanu, M.: This before that: causal precedence in the biomedical domain. arXiv preprint arXiv:​1606.​08089 (2016)
19.
Yin, W., Kann, K., Yu, M., Schütze, H.: Comparative study of CNN and RNN for natural language processing. arXiv preprint arXiv:​1702.​01923 (2017)
20.
21.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRef
22.
Galley, M., McKeown, K.: Improving word sense disambiguation in lexical chaining (2003)
23.
Morris, J., Hirst, G.: Lexical cohesion computed by thesaural relations as an indicator of the structure of text. Comput. Linguisti. 17(1), 21–48 (1991)
24.
Mascarell, L.: Lexical chains meet word embeddings in document-level statistical machine translation. In: Proceedings of the Third Workshop on Discourse in Machine Translation, pp. 99–109 (2017)
25.
26.
Arora, S., Liang, Y., Ma, T.: A simple but tough-to-beat baseline for sentence embeddings (2016)
27.
Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., Bengio, Y.: Theano: a CPU and GPU math compiler in Python. In: Proceedings of the 9th Python in Science Conference, pp. 1–7 (2010)
28.
Bird, S., Dale, R., Dorr, B.J., Gibson, B.R., Joseph, M.T., Kan, M.Y., Lee, D., Powley, B., Radev, D.R., Tan, Y.F., et al.: The ACL anthology reference corpus: a reference dataset for bibliographic research in computational linguistics. In: LREC (2008)
Metadata
Title
Leveraging Document-Specific Information for Classifying Relations in Scientific Articles
Authors
Qin Dai
Naoya Inoue
Paul Reisert
Kentaro Inui
Copyright Year
2018
Book
New Frontiers in Artificial Intelligence

Print ISBN: 978-3-319-93793-9

Electronic ISBN: 978-3-319-93794-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-93794-6_26

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