【摘要】 针对旅游文本噪声多、景点多且展示不直观的问题,提出一种基于概率主题模型的景点-主题模型。模型假设同一篇文档涉及多个具有相关关系的景点,引入"全局景点"过滤噪声语义,并利用Gibbs采样算法估计最大似然函数的参数,获取目的地景点的主题分布。实验通过对景点主题特征进行聚类,评估聚类效果从而间接评价模型训练效果,并定性分析"全局景点"对模型的作用。实验结果表明,该模型对旅游文本的建模效果优于基准算法TF-IDF与隐含狄利克雷分布(LDA),且"全局景点"的引入对建模效果有明显的改善作用。最后通过景点关联图的方式对实验结果进行可视化展示。更多还原

【Abstract】 Since the tourism text for destinations contains semantic noise and different scenic spots, which can not be displayed intuitively, a new scenic spots-topic model based on the probabilistic topic model was proposed. The model assumed that one document included several scenic spots with correlation, and a special scenic spot named  global scenic spot was introduced to filter the semantic noise. Then Gibbs sampling algorithm was employed to learn the maximum a posteriori estimates of the model and get a topic distribution vector for each scenic spot. A clustering experiment was conducted to indirectly evaluate the effects of the model and analyze the impact of global scenic spot on the model. The result shows that the proposed model has better effect than baseline model such as TF-IDF( Term Frequency-Inverse Document Frequency) and Latent Dirichlet Allocation( LDA), and the global scenic spot can improve the modeling effect significantly. Finally, scenic spots association graph was employed to display the result visually.更多还原