Evaluating the contribution of framenet to gender-based violence identification - How semantic annotation can be used as a resource for identifying patterns of violence

Abstract

According to the World Health Organization, one in three women has been a victim of physical or sexual violence by their partner at some point in their lives. This indicates that Gender-Based Violence is a global public health concern. In Brazil, the scenario is not different. Although healthcare professionals are required to report violence cases, underreporting is still a challenge where Gender-Based Violence (GBV) is concerned. Different factors influence this, such as the fear of the victims, health professionals’ difficulties in identifying episodes of violence, and the lack of support tools for the health teams. When we also consider the lack of integration between the public information systems in Brazil, the difficulty in tackling the problem only increases. Considering that, a collaborative initiative between FrameNet Brasil and Vital Strategies Brasil launched the project “Data Linkage and Frame-Based Textual Analysis for the Identification of Candidate Cases of Gender-Based Violence in Territories”. The goal of the project is to develop tools for early warning and intervention, offering enhanced support for health teams, local authorities, and policymakers by employing linguistic analysis methods to read and map patterns within the open-text fields of electronic medical records completed in health units. Developed within this project, the main goal of this master’s thesis is to examine whether semantic annotation according to the FrameNet methodology can contribute with sufficient information to enhance the identification of potential cases and patterns of gender-based violence. To do that, a quantitative and qualitative evaluation of the outcome of the Data-Linkage project was performed, which involved a comparative assessment of an SVM model’s performance trained with: (1) data from the open-text fields which were annotated manually and automatically for frames and frame elements, (2) the data in (1) augmented with annotated parameterized data and (3) parameterized data only, without any annotation. Additionally, the qualitative evaluation also assessed the annotation process for both manual and automatic approaches. Following that, we were able to answer our research question and corroborate the hypothesis that the application of FrameNet methodology can help identify patterns and cases of violence. The quantitative assessment showed that the semantic models had over 0.3 of leverage on the F1 score compared to the categorical model. Our qualitative analysis validated the methods employed, suggested improvements, and indicated possible patterns to be further studied in future work.