Predicting Themes within Complex Unstructured Texts: A Case Study on Safeguarding Reports
This work addresses the challenge of text classification in specialized domains like safeguarding, where data is scarce, but it is incremental as it combines existing methods.
The paper tackled the problem of automatically identifying main themes in safeguarding reports, a domain with limited labeled data, and found that deep learning models can simulate expert behavior effectively for this complex task.
The task of text and sentence classification is associated with the need for large amounts of labelled training data. The acquisition of high volumes of labelled datasets can be expensive or unfeasible, especially for highly-specialised domains for which documents are hard to obtain. Research on the application of supervised classification based on small amounts of training data is limited. In this paper, we address the combination of state-of-the-art deep learning and classification methods and provide an insight into what combination of methods fit the needs of small, domain-specific, and terminologically-rich corpora. We focus on a real-world scenario related to a collection of safeguarding reports comprising learning experiences and reflections on tackling serious incidents involving children and vulnerable adults. The relatively small volume of available reports and their use of highly domain-specific terminology makes the application of automated approaches difficult. We focus on the problem of automatically identifying the main themes in a safeguarding report using supervised classification approaches. Our results show the potential of deep learning models to simulate subject-expert behaviour even for complex tasks with limited labelled data.