Extraction of causal relations based on SBEL and BERT model

LERCause: Deep learning approaches for causal sentence identification from nuclear safety reports

Identifying causal sentences from nuclear incident reports is essential for advancing nuclear safety research and applications. Nonetheless, accurately locating and labeling causal sentences in text data is challenging, and might benefit from the usage of automated techniques. In this paper, we introduce LERCause, a labeled dataset combined with labeling methods meant to serve as a foundation for the classification of causal sentences in the domain of nuclear safety. We used three BERT models (BERT, BioBERT, and SciBERT) to 10,608 annotated sentences from the Licensee Event Report (LER) corpus for predicting sentence labels (Causal vs. non-Causal). We also used a keyword-based heuristic strategy, three standard machine learning methods (Logistic Regression, Gradient Boosting, and Support Vector Machine), and a deep learning approach (Convolutional Neural Network; CNN) for comparison. We found that the BERT-centric models outperformed all other tested models in terms of all evaluation metrics (accuracy, precision, recall, and F1 score). BioBERT resulted in the highest overall F1 score of 94.49% from the ten-fold cross-validation. Our dataset and coding framework can provide a robust baseline for assessing and comparing new causal sentences extraction techniques. As far as we know, our research breaks new ground by leveraging BERT-centric models for causal sentence classification in the nuclear safety domain and by openly distributing labeled data and code to enable reproducibility in subsequent research.

Joint learning-based causal relation extraction from biomedical literature

Causal relation extraction of biomedical entities is one of the most complex tasks in biomedical text mining, which involves two kinds of information: entity relations and entity functions. One feasible approach is to take relation extraction and function detection as two independent sub-tasks. However, this separate learning method ignores the intrinsic correlation between them and leads to unsatisfactory performance. In this paper, we propose a joint learning model, which combines entity relation extraction and entity function detection to exploit their commonality and capture their inter-relationship, so as to improve the performance of biomedical causal relation extraction. Experimental results on the BioCreative-V Track 4 corpus show that our joint learning model outperforms the separate models in BEL statement extraction, achieving the F1 scores of 57.0% and 37.3% on the test set in Stage 2 and Stage 1 evaluations, respectively. This demonstrates that our joint learning system reaches the state-of-the-art performance in Stage 2 compared with other systems.

Nanopublication-based semantic publishing and reviewing: a field study with formalization papers

With the rapidly increasing amount of scientific literature, it is getting continuously more difficult for researchers in different disciplines to keep up-to-date with the recent findings in their field of study. Processing scientific articles in an automated fashion has been proposed as a solution to this problem, but the accuracy of such processing remains very poor for extraction tasks beyond the most basic ones (like locating and identifying entities and simple classification based on predefined categories). Few approaches have tried to change how we publish scientific results in the first place, such as by making articles machine-interpretable by expressing them with formal semantics from the start. In the work presented here, we propose a first step in this direction by setting out to demonstrate that we can formally publish high-level scientific claims in formal logic, and publish the results in a special issue of an existing journal. We use the concept and technology of nanopublications for this endeavor, and represent not just the submissions and final papers in this RDF-based format, but also the whole process in between, including reviews, responses, and decisions. We do this by performing a field study with what we call formalization papers, which contribute a novel formalization of a previously published claim. We received 15 submissions from 18 authors, who then went through the whole publication process leading to the publication of their contributions in the special issue. Our evaluation shows the technical and practical feasibility of our approach. The participating authors mostly showed high levels of interest and confidence, and mostly experienced the process as not very difficult, despite the technical nature of the current user interfaces. We believe that these results indicate that it is possible to publish scientific results from different fields with machine-interpretable semantics from the start, which in turn opens countless possibilities to radically improve in the future the effectiveness and efficiency of the scientific endeavor as a whole.