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Protocol for a reproducible experimental survey on biomedical sentence similarity

Author

Listed:
  • Alicia Lara-Clares
  • Juan J Lastra-Díaz
  • Ana Garcia-Serrano

Abstract

Measuring semantic similarity between sentences is a significant task in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and biomedical text mining. For this reason, the proposal of sentence similarity methods for the biomedical domain has attracted a lot of attention in recent years. However, most sentence similarity methods and experimental results reported in the biomedical domain cannot be reproduced for multiple reasons as follows: the copying of previous results without confirmation, the lack of source code and data to replicate both methods and experiments, and the lack of a detailed definition of the experimental setup, among others. As a consequence of this reproducibility gap, the state of the problem can be neither elucidated nor new lines of research be soundly set. On the other hand, there are other significant gaps in the literature on biomedical sentence similarity as follows: (1) the evaluation of several unexplored sentence similarity methods which deserve to be studied; (2) the evaluation of an unexplored benchmark on biomedical sentence similarity, called Corpus-Transcriptional-Regulation (CTR); (3) a study on the impact of the pre-processing stage and Named Entity Recognition (NER) tools on the performance of the sentence similarity methods; and finally, (4) the lack of software and data resources for the reproducibility of methods and experiments in this line of research. Identified these open problems, this registered report introduces a detailed experimental setup, together with a categorization of the literature, to develop the largest, updated, and for the first time, reproducible experimental survey on biomedical sentence similarity. Our aforementioned experimental survey will be based on our own software replication and the evaluation of all methods being studied on the same software platform, which will be specially developed for this work, and it will become the first publicly available software library for biomedical sentence similarity. Finally, we will provide a very detailed reproducibility protocol and dataset as supplementary material to allow the exact replication of all our experiments and results.

Suggested Citation

  • Alicia Lara-Clares & Juan J Lastra-Díaz & Ana Garcia-Serrano, 2021. "Protocol for a reproducible experimental survey on biomedical sentence similarity," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-28, March.
    • Handle: RePEc:plo:pone00:0248663
    DOI: 10.1371/journal.pone.0248663
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    References listed on IDEAS

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    1. Hamed Hassanzadeh & Tudor Groza & Anthony Nguyen & Jane Hunter, 2015. "A Supervised Approach to Quantifying Sentence Similarity: With Application to Evidence Based Medicine," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-25, June.
    2. Haibin Liu & Lawrence Hunter & Vlado Kešelj & Karin Verspoor, 2013. "Approximate Subgraph Matching-Based Literature Mining for Biomedical Events and Relations," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-16, April.
    3. Tomoyuki Kajiwara & Danushka Bollegala & Yuichi Yoshida & Ken-ichi Kawarabayashi, 2017. "An iterative approach for the global estimation of sentence similarity," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-15, September.
    4. Yue Shang & Yanpeng Li & Hongfei Lin & Zhihao Yang, 2011. "Enhancing Biomedical Text Summarization Using Semantic Relation Extraction," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-10, August.
    5. Kevin W Boyack & David Newman & Russell J Duhon & Richard Klavans & Michael Patek & Joseph R Biberstine & Bob Schijvenaars & André Skupin & Nianli Ma & Katy Börner, 2011. "Clustering More than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text-Based Similarity Approaches," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-11, March.
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