Incorporating Zoning Information into Argument Mining from Biomedical Literature
Boyang Liu, Viktor Schlegel, Riza Batista-Navarro, Sophia Ananiadou
Abstract
The goal of text zoning is to segment a text into zones (i.e., Background, Conclusion) that serve distinct functions. Argumentative zoning, a specific text zoning scheme for the scientific domain, is considered as the antecedent for argument mining by many researchers. Surprisingly, however, little work is concerned with exploiting zoning information to improve the performance of argument mining models, despite the relatedness of the two tasks. In this paper, we propose two transformer-based models to incorporate zoning information into argumentative component identification and classification tasks. One model is for the sentence-level argument mining task and the other is for the token-level task. In particular, we add the zoning labels predicted by an off-the-shelf model to the beginning of each sentence, inspired by the convention commonly used biomedical abstracts. Moreover, we employ multi-head attention to transfer the sentence-level zoning information to each token in a sentence. Based on experiment results, we find a significant improvement in F1-scores for both sentence- and token-level tasks. It is worth mentioning that these zoning labels can be obtained with high accuracy by utilising readily available automated methods. Thus, existing argument mining models can be improved by incorporating zoning information without any additional annotation cost.- Anthology ID:
- 2022.lrec-1.663
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 6162–6169
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.663
- DOI:
- Cite (ACL):
- Boyang Liu, Viktor Schlegel, Riza Batista-Navarro, and Sophia Ananiadou. 2022. Incorporating Zoning Information into Argument Mining from Biomedical Literature. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6162–6169, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Incorporating Zoning Information into Argument Mining from Biomedical Literature (Liu et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2022.lrec-1.663.pdf
- Data
- PubMed RCT