A Novel End-To-End Event Geolocation Method Leveraging Hyperbolic Space and Toponym Hierarchies

Timely detection and geolocation of events based on social data can provide critical information for applications such as crisis response and resource allocation. However, most existing methods are greatly affected by event detection errors, leading to insufficient geolocation accuracy. To this end, this paper proposes a novel end-to-end event geolocation method (GTOP) leveraging Hyperbolic space and toponym hierarchies. Specifically, the proposed method contains one event detection module and one geolocation module. The event detection module constructs a heterogeneous information networks based on social data, and then constructs a homogeneous message graph and combines it with the text and time feature of the message to learning initial features of nodes. Node features are updated in Hyperbolic space and then fed into a classifier for event detection. To reduce the geolocation error, this paper proposes a noise toponym filtering algorithm (HIST) based on the hierarchical structure of toponyms. HIST analyzes the hierarchical structure of toponyms mentioned in the event cluster, taking the highly frequent city-level locations as the coarse-grained locations for events. By comparing the hierarchical structure of the toponyms within the cluster against those of the coarse-grained locations of events, HIST filters out noisy toponyms. To further improve the geolocation accuracy, we propose a fine-grained pseudo toponyms generation algorithm (FIT) based on the output of HIST, and combine generated pseudo toponyms with filtered toponyms to locate events based on the geographic center points of the combined toponyms. Extensive experiments are conducted on the Chinese dataset constructed in this paper and another public English dataset. The experimental results show that the proposed method is superior to the state-of-the-art baselines.

CRENER: A Character Relation Enhanced Chinese NER Model

Chinese Named Entity Recognition (NER) is an important task in information extraction, which has a significant impact on downstream applications. Due to the lack of natural separators in Chinese, previous NER methods mostly relied on external dictionaries to enrich the semantic and boundary information of Chinese words. However, such methods may introduce noise that affects the accuracy of named entity recognition. To this end, we propose a character relation enhanced Chinese NER model (CRENER). This model defines four types of tags that reflect the relationships between characters, and proposes a fine-grained modeling of the relationships between characters based on three types of relationships: adjacency relations between characters, relations between characters and tags, and relations between tags, to more accurately identify entity boundaries and improve Chinese NER accuracy. Specifically, we transform the Chinese NER task into a character-character relationship classification task, ensuring the accuracy of entity boundary recognition through joint modeling of relation tags. To enhance the model's ability to understand contextual information, WRENER further constructed an adapted transformer encoder that combines unscaled direction-aware and distance-aware masked self-attention mechanisms. Moreover, a relationship representation enhancement module was constructed to model predefined relationship tags, effectively mining the relationship representations between characters and tags. Experiments conducted on four well-known Chinese NER benchmark datasets have shown that the proposed model outperforms state-of-the-art baselines. The ablation experiment also demonstrated the effectiveness of the proposed model.