Chinese Entity Relation Extraction for Enhancing Public Event Analysis in Weibo

Extracting public events from social networks like Weibo is crucial for understanding them, as it involves identifying key elements such as entities and relationships. Rapidly parsing these elements from social networks can enhance situational awareness and decision-making processes. However, in the realm of Chinese open entity relation extraction, especially from those informal texts in social networks, the effectiveness of supervised methods is hindered by the need for extensive relation-type annotation, coupled with the challenge posed by the non-standardized expression of Chinese sentence patterns. This study introduces an innovative approach to Chinese open entity relation extraction, with a particular focus on extracting public events from texts in Weibo, one of the most popular social networks, by leveraging an improved BERT model. The approach employs a [Formula: see text] model to identify entity pairs within sentences. Additionally, five predefined syntactic rules are introduced, and entity relations, especially those related to public events, are extracted through a fusion of BERT-based techniques and rules. The proposed method is evaluated through training on a self-built corpus, as well as the cluner2020 and nlpcc2019 datasets. The experimental results highlight the effectiveness of the proposed enhanced BERT-based approach, demonstrating the suitability for Chinese open entity relation extraction, particularly in identifying and categorizing public events. Furthermore, this study not only advances entity relation extraction methodologies but also contributes to the management domain by offering practical solutions to the challenges posed by Chinese linguistic complexities, providing insights into the analysis and management of public events.