Extracting causal relations from the literature with word vector mapping

Railway accident causation analysis: Current approaches, challenges and potential solutions

Railway accident causation analysis is fundamental to understanding the nature of railway safety. Although a considerable number of prior studies have investigated this context, many of them suffer from the need to deal with a large amount of textual data given that most railway safety-related information is recorded and stored in the form of text. To gain a better understanding of the limitations imposed by overreliance on textual analysis, a scoping review of the academic literature on how railway accident causation analysis is addressed has been conducted. The results confirm the high frequency of using textual data, a single case study, and in-depth analysis frameworks. While the value of exploring causational factors is clear, the high level of human intervention and the labour-intensive analysis processes based on a large volume of textual data hinder researchers from understanding the complex nature of the rail safety system. Recently, growing attention has been given to the application of Natural Language Processing (NLP) to aid the practice of analysing a large corpus of textual data, but only limited studies to date in railway safety use such techniques and none address railway accident causation analysis. To fill this gap, a supplementary review is conducted to identify opportunities, challenges, boundaries and limitations in the application of NLP approaches to railway accident causation analysis. Findings indicate that novel techniques using off-the-shelf tools have strong potential to overcome the limitations of overreliance on manual analysis in practice and theory, but the absence of shared railway safety-related benchmark corpora restricts implementation. This study sheds light on a new approach to railway accident causation analysis and clarifies future applicable utilisations for further research.

EMSI-BERT: Asymmetrical Entity-Mask Strategy and Symbol-Insert Structure for Drug–Drug Interaction Extraction Based on BERT

Drug-drug interaction (DDI) extraction has seen growing usage of deep models, but their effectiveness has been restrained by limited domain-labeled data, a weak representation of co-occurring entities, and poor adaptation of downstream tasks. This paper proposes a novel EMSI-BERT method for drug–drug interaction extraction based on an asymmetrical Entity-Mask strategy and a Symbol-Insert structure. Firstly, the EMSI-BERT method utilizes the asymmetrical Entity-Mask strategy to address the weak representation of co-occurring entity information using the drug entity dictionary in the pre-training BERT task. Secondly, the EMSI-BERT method incorporates four symbols to distinguish different entity combinations of the same input sequence and utilizes the Symbol-Insert structure to address the week adaptation of downstream tasks in the fine-tuning stage of DDI classification. The experimental results showed that EMSI-BERT for DDI extraction achieved a 0.82 F1-score on DDI-Extraction 2013, and it improved the performances of the multi-classification task of DDI extraction and the two-classification task of DDI detection. Compared with baseline Basic-BERT, the proposed pre-training BERT with the asymmetrical Entity-Mask strategy could obtain better effects in downstream tasks and effectively limit “Other” samples’ effects. The model visualization results illustrated that EMSI-BERT could extract semantic information at different levels and granularities in a continuous space.

A practical approach towards causality mining in clinical text using active transfer learning

OBJECTIVE

Causality mining is an active research area, which requires the application of state-of-the-art natural language processing techniques. In the healthcare domain, medical experts create clinical text to overcome the limitation of well-defined and schema driven information systems. The objective of this research work is to create a framework, which can convert clinical text into causal knowledge.

METHODS

A practical approach based on term expansion, phrase generation, BERT based phrase embedding and semantic matching, semantic enrichment, expert verification, and model evolution has been used to construct a comprehensive causality mining framework. This active transfer learning based framework along with its supplementary services, is able to extract and enrich, causal relationships and their corresponding entities from clinical text.

RESULTS

The multi-model transfer learning technique when applied over multiple iterations, gains substantial performance improvements. We also present a comparative analysis of the presented techniques with their common alternatives, which demonstrate the correctness of our approach and its ability to capture most causal relationships.

CONCLUSION

The presented framework has provided cutting-edge results in the healthcare domain. However, the framework can be tweaked to provide causality detection in other domains, as well.

SIGNIFICANCE

The presented framework is generic enough to be utilized in any domain, healthcare services can gain massive benefits due to the voluminous and various nature of its data. This causal knowledge extraction framework can be used to summarize clinical text, create personas, discover medical knowledge, and provide evidence to clinical decision making.

Mapping Phenomena Relevant to Adolescent Emotion Regulation: A Text-Mining Systematic Review

Adolescence is a developmentally sensitive period for emotion regulation with potentially lifelong implications for mental health and well-being. Although substantial empirical research has addressed this topic, the literature is fragmented across subdisciplines, and an overarching theoretical framework is lacking. The first step toward constructing a unifying framework is identifying relevant phenomena. This systematic review of 6305 articles used text mining to identify phenomena relevant to adolescents' emotion regulation. First, a baseline was established of relevant phenomena discussed in theory and recent narrative reviews. Then, article keywords and abstracts were analyzed using text mining, examining term frequency as an indicator of relevance and term co-occurrence as an indicator of association. The results reflected themes commonly featured in theory and narrative reviews, such as socialization and neurocognitive development, but also identified undertheorized themes, such as developmental disorders, physical health, external stressors, structural disadvantage, substance use, identity and moral development, and sexual development. The findings illustrate how text mining systematic reviews, a novel approach, may complement narrative reviews. Future theoretical work might integrate these undertheorized themes into an overarching framework, and empirical research might consider them as promising areas for future research, or as potential confounders in research on adolescents' emotion regulation.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40894-021-00160-7.