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Dual architecture for name entity extraction and relation extraction with applications in medical corpora: student research abstract

Published: 06 May 2022 Publication History

Abstract

There is a growing interest in automatic knowledge discovery in plain text documents. Automation enables the analysis of massive collections of information. Such efforts are relevant in the health domain which has a large volume of available resources to transform areas important for society when addressing various health research challenges. However, knowledge discovery is usually aided by annotated corpora, which are scarce resources in the literature. This work considers as a start point existent health-oriented Spanish dataset. In addition, it also creates an English variant using the same tagging system. Furthermore, we design and analyze two separated architectures for Entity Extraction and Relation Recognition that outperform previous works in the Spanish dataset. We also evaluate their performance in the English version with such promising results. Finally, we perform a use case experiment to evaluate the utility of the output of these two architectures in Information Retrieval systems.

References

[1]
2020. UH-MAJA-KD at eHealth-KD Challenge 2020: Deep Learning. (2020).
[2]
Vera Boteva, Demian Gholipour, Artem Sokolov, and Stefan Riezler. 2016. A full-text learning to rank dataset for medical information retrieval. In European Conference on Information Retrieval. Springer, 716--722.
[3]
Xiang Dai and Heike Adel. 2020. An analysis of simple data augmentation for named entity recognition. arXiv preprint arXiv:2010.11683 (2020).
[4]
Aitor García-Pablos, Naiara Perez, Montse Cuadros, and Elena Zotova. 2020. Vicomtech at eHealth-KD Challenge 2020: Deep End-to-End Model for Entity and Relation Extraction in Medical Text. In Proceedings of the Iberian Languages Evaluation Forum co-located with 36th Conference of the Spanish Society for Natural Language Processing, IberLEF@ SEPLN, Vol. 2020.
[5]
Jing Li, Aixin Sun, Jianglei Han, and Chenliang Li. 2020. A survey on deep learning for named entity recognition. IEEE Transactions on Knowledge and Data Engineering (2020).
[6]
Yang Liu, Furu Wei, Sujian Li, Heng Ji, Ming Zhou, and Houfeng Wang. 2015. A dependency-based neural network for relation classification. arXiv preprint arXiv:1507.04646 (2015).
[7]
Sachin Pawar, Girish K Palshikar, and Pushpak Bhattacharyya. 2017. Relation extraction: A survey. arXiv preprint arXiv:1712.05191 (2017).
[8]
Alejandro Piad-Morffis, Yoan Gutiérrez, Yudivian Almeida-Cruz, and Rafael Muñoz. 2020. A computational ecosystem to support eHealth Knowledge Discovery technologies in Spanish. Journal of biomedical informatics 109 (2020).
[9]
Alejandro Piad-Morffis, Yoan Gutiérrez, Suilan Estevez-Velarde, Yudivián Almeida-Cruz, Rafael Muñoz, and Andrés Montoyo. 2020. Overview of the eHealth Knowledge Discovery Challenge at IberLEF 2020. In Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020).
[10]
Filip Radlinski and Nick Craswell. 2010. Comparing the sensitivity of information retrieval metrics. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. 667--674.
[11]
Kai Sheng Tai, Richard Socher, and Christopher D Manning. 2015. Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 1556--1566.
[12]
Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, and Iryna Gurevych. 2021. BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models. arXiv preprint arXiv:2104.08663 (2021).
[13]
Andrew Viterbi. 1967. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE transactions on Information Theory 13, 2 (1967), 260--269.
[14]
Renzo M Rivera Zavala, Paloma Martínez, and Isabel Segura-Bedmar. 2018. A Hybrid Bi-LSTM-CRF model for Knowledge Recognition from eHealth documents. In TASS@ SEPLN. 65--70.

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  1. Dual architecture for name entity extraction and relation extraction with applications in medical corpora: student research abstract

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      cover image ACM Conferences
      SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing
      April 2022
      2099 pages
      ISBN:9781450387132
      DOI:10.1145/3477314
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      Published: 06 May 2022

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      1. deep learning
      2. information retrieval
      3. ontology learning

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      • Red Hat Research

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