Grading System

Extracting entity connectivity from texts is important for uncovering how places relate within real-world discourse. While structured data is informative, textual data captures rich contextual and semantic knowledge enabling us to identify hidden networks of interdependence and thematic connections among geographic entities. Entity connectivity is not just complementary to information retrieval, but rather essential in various activities including event analysis, spatial decision support systems, urban studies, and knowledge graph development. This research presents a grading system for evaluating connectivity between geospatial entities, such as places, events, and geopolitical entities extracted from texts. The methodology proposes two versions of the grading system: one based on co-occurrences and semantic similarity, and a second one that additionally incorporates the geodesic distance feature. The two systems are evaluated and compared using six machine learning algorithms: Random Forest, Gradient Boosting, Multi-Layer Perceptron (MLP), K-Nearest Neighbors (KNN), Decision Tree, and Support Vector Machine (SVM). The performance of the algorithms is analyzed by measuring accuracy, precision, recall, F1-score, and R². The results show that the system without geodesic distance performs better on general texts, indicating that the addition of geographic features can introduce noise in cases where geographic relevance is not immediately important. In terms of accuracy, Decision Tree was the best performing algorithm for the system without geodesic distance, whereas K-Nearest Neighbors (KNN) was the best performing model for the system with geodesic distance, which indicates that different feature sets may benefit from different learning strategies. You can find attached the data used for training and the trained classifiers for both the 2 versions of the grading system.

从文本中提取实体关联关系，对于揭示现实话语场景中地理实体间的关联机制至关重要。尽管结构化数据具备信息价值，但文本数据蕴含丰富的语境与语义知识，可助力我们挖掘地理实体间潜藏的依存关联网络与主题连接关系。实体关联关系不仅是信息检索的补充手段，更在事件分析、空间决策支持系统、城市研究以及知识图谱构建等诸多场景中具备核心价值。本研究提出一套评分体系，用于评估从文本中提取的地理空间实体（geospatial entities，涵盖场所、事件及地缘政治实体）之间的关联程度。该方法论构建了两套评分体系版本：其一基于共现关系与语义相似度，其二则额外引入了测地距离（geodesic distance）特征。本研究采用六种机器学习算法对两套评分体系进行评估与对比：随机森林（Random Forest）、梯度提升树（Gradient Boosting）、多层感知机（Multi-Layer Perceptron, MLP）、K近邻（K-Nearest Neighbors, KNN）、决策树（Decision Tree）以及支持向量机（Support Vector Machine, SVM）。通过准确率（accuracy）、精确率（precision）、召回率（recall）、F1值（F1-score）以及决定系数（R²）等指标，对各算法的性能展开分析。实验结果表明，未引入测地距离特征的评分体系在通用文本场景下表现更优，这说明当地理相关性并非核心考量因素时，新增地理特征可能会引入噪声。从准确率指标来看，未引入测地距离的评分体系最优算法为决策树（Decision Tree），而引入测地距离的评分体系最优模型则为K近邻（K-Nearest Neighbors, KNN），这表明不同的特征集可通过适配不同的学习策略以获得更佳性能。本研究附带了两套评分体系所用的训练数据与训练完成的分类器文件。