基于跨语言词嵌入的多语言新闻聚类研究
Research on Multilingual News Clustering Based on Cross-Language Word Embeddings
AI总结：
本文研究跨语言新闻聚类问题，提出用知识蒸馏训练中英跨语言句向量模型，并结合LDA主题分布表示新闻，同时改进Single-Pass增量聚类算法，以高效实现多语言新闻事件分类。
摘要：
对不同国家报道的同一事件进行分类，对于舆情控制和情报收集具有重要意义。由于新闻类型多样，仅依赖人工翻译成本高且效率低，而仅依赖翻译系统则在调用翻译接口和存储译文方面会产生相当大的性能开销。为了解决这一问题，我们主要关注跨语言新闻的聚类问题。具体而言，我们使用新闻标题在混合语义空间中的句向量表示与新闻内容的主题概率分布相结合来表示一篇新闻文章。在跨语言模型的训练中，我们采用知识蒸馏技术将两个语义空间拟合到一个混合语义空间。我们放弃了K-Means和AGNES等传统静态聚类方法，转而采用增量聚类算法Single-Pass，并对其进一步改进，以更好地适应跨语言新闻聚类场景。我们的主要贡献如下：（1）我们采用英文标准BERT作为教师模型，XLM-Roberta作为学生模型，通过知识蒸馏训练一个能够表示中英双语句子级文本的跨语言模型。（2）我们使用LDA主题模型将新闻表示为标题的跨语言向量与内容的主题概率分布的组合，引入主题相似度等概念来解决新闻内容表示中的跨语言问题。（3）我们针对新闻语境对Single-Pass聚类算法进行了适配，使其更具适用性。我们对Single-Pass的优化包括调整样本与簇之间的距离算法、增加簇合并操作以及引入新闻时间参数。
英文摘要：
Classifying the same event reported by different countries is of significant importance for public opinion control and intelligence gathering. Due to the diverse types of news, relying solely on transla-tors would be costly and inefficient, while depending solely on translation systems would incur considerable performance overheads in invoking translation interfaces and storing translated texts. To address this issue, we mainly focus on the clustering problem of cross-lingual news. To be specific, we use a combination of sentence vector representations of news headlines in a mixed semantic space and the topic probability distributions of news content to represent a news article. In the training of cross-lingual models, we employ knowledge distillation techniques to fit two semantic spaces into a mixed semantic space. We abandon traditional static clustering methods like K-Means and AGNES in favor of the incremental clustering algorithm Single-Pass, which we further modify to better suit cross-lingual news clustering scenarios. Our main contributions are as follows: (1) We adopt the English standard BERT as the teacher model and XLM-Roberta as the student model, training a cross-lingual model through knowledge distillation that can represent sentence-level bilingual texts in both Chinese and English. (2) We use the LDA topic model to represent news as a combina-tion of cross-lingual vectors for headlines and topic probability distributions for con-tent, introducing concepts such as topic similarity to address the cross-lingual issue in news content representation. (3) We adapt the Single-Pass clustering algorithm for the news context to make it more applicable. Our optimizations of Single-Pass include ad-justing the distance algorithm between samples and clusters, adding cluster merging operations, and incorporating a news time parameter.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
