论文标题
多模式新闻检索的功能分析
A Feature Analysis for Multimodal News Retrieval
论文作者
论文摘要
基于内容的信息检索基于文档中包含的信息，而不是使用元数据，例如关键字。大多数信息检索方法是基于文本或图像。在本文中，我们调查了多模式特征在各个领域中的跨语言新闻搜索的有用性：政治，健康，环境，体育和金融。为此，我们考虑五种用于图像和文本的功能类型，并使用不同的组合比较检索系统的性能。实验结果表明，在考虑视觉和文本信息时，可以改善检索结果。此外，可以观察到，在文本特征中，实体在检索任务中的视觉特征之间的地理位置嵌入在视觉特征之间具有更好的性能。
Content-based information retrieval is based on the information contained in documents rather than using metadata such as keywords. Most information retrieval methods are either based on text or image. In this paper, we investigate the usefulness of multimodal features for cross-lingual news search in various domains: politics, health, environment, sport, and finance. To this end, we consider five feature types for image and text and compare the performance of the retrieval system using different combinations. Experimental results show that retrieval results can be improved when considering both visual and textual information. In addition, it is observed that among textual features entity overlap outperforms word embeddings, while geolocation embeddings achieve better performance among visual features in the retrieval task.
