Detecting Fields of Application in Biomedical Abstracts Using Argumentative Elements

Основні поняття

Argumentative elements in biomedical abstracts can be leveraged to improve the detection of fields of application, such as disease diagnosis, mechanism, and therapy development.

Анотація

The author investigated the use of argumentative elements extracted from biomedical abstracts to improve the classification of the fields of application covered in the articles. The study was conducted on a new corpus of over 2,000 biomedical abstracts, which were annotated with eight labels representing different fields of application.
The author first reviewed available tools for extracting argumentative elements from text and selected three tools that could be successfully applied to the corpus: ArguminSci, HSLN, and MARGOT. Experiments were conducted using the PubMedBERT model, fine-tuned on the corpus, to classify the abstracts based on the title and abstract alone, as well as using the argumentative elements extracted by the different tools.
The results showed that using certain argumentative elements, such as the "conclusion" and "background" sections, could outperform the baseline of using the title and abstract alone. The top F1 scores ranged from 0.22 to 0.84, depending on the field of application. The author also analyzed the overlap between the argumentative elements extracted by the different tools and found that they did not always correlate well, suggesting that some elements may be more informative than others for the task.
The author concludes that the new corpus and the insights on the usefulness of argumentative elements for classifying fields of application in biomedical abstracts can be valuable for future research in this area.

Статистика

Overexpression of S6K1 was detected in [...] breast cancer, and correlated with the worse disease outcome.
softer substrates such as osteoblasts and other bone cells result in a much altered unloading response as well as significant plastic deformation. These substrates are relevant to metastasis...
Assessment of anti-cancer drug efficacy in in vitro three-dimensional (3D) bioengineered cancer models provides [...] towards pre-clinical translation of potential drug candidates.

Цитати

"Focusing on particular facts, instead of the com-plete text, can potentially improve searching for specific information in the scientific literature."
"Argumentative elements allow fo-cusing on specific parts of a publication, e.g., the background section or the claims from the authors."
"The best argumentative labels were the ones related the conclusion and background sections of an abstract."

Ключові висновки, отримані з

Detection of fields of applications in biomedical abstracts with the support of argumentation elements

by Mariana Neve... о arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.06121.pdf

Глибші Запити

How could the insights from this study be applied to other text mining tasks in the biomedical domain, beyond just classifying fields of application

The insights from this study can be applied to various text mining tasks in the biomedical domain beyond just classifying fields of application. For instance, the tools and techniques used to extract argumentative elements can be utilized in sentiment analysis of biomedical literature to understand the tone and attitude towards certain topics or findings. Additionally, these tools can aid in summarizing complex scientific articles by identifying key arguments and conclusions, making it easier for researchers to grasp the main points quickly. Moreover, the classification approach can be adapted to identify trends or patterns in research topics, helping researchers stay updated on the latest advancements in their field.

What are some potential limitations or biases in the corpus used in this study, and how might they affect the generalizability of the findings

One potential limitation of the corpus used in this study is the source bias, as the articles were derived from reports by the European Commission, which may not represent the entire spectrum of biomedical literature. This bias could affect the generalizability of the findings, as the corpus may not capture the diversity of research topics and methodologies present in the broader biomedical domain. Additionally, the imbalance in the distribution of labels across the corpus could lead to skewed results, especially for less represented fields of application. Researchers should be cautious when extrapolating the results to a broader context and consider using more diverse and representative datasets for validation.

Given the varying performance of the different argumentative elements, how could an ensemble approach that combines multiple elements be explored to further improve the classification accuracy

To improve classification accuracy, an ensemble approach that combines multiple argumentative elements could be explored. By leveraging the strengths of different elements, such as background information, conclusions, and evidence, researchers can create a more robust model that considers various aspects of the text. This ensemble method could involve weighting the predictions of each element based on their individual performance or using a voting system to determine the final classification. By integrating multiple sources of information, researchers can enhance the model's ability to capture nuanced relationships and improve overall accuracy in classifying fields of application in biomedical abstracts.