TAP-DLND 1.0：一个文档级新颖性检测语料库
TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection
摘要：
检测整个文档的新颖性是一个人工智能（AI）前沿问题，具有广泛的NLP应用，例如提取文档摘要，跟踪新闻事件的发展，预测学术文章的影响等。虽然问题很重要，但我们不知道任何基准文档级数据可以正确地解决分类框架中自动新颖性检测技术的评估。为了弥合这一差距，我们在这里提出了一个资源，基准测试的技术，文档级的新颖性检测。我们通过定期对多个域中的新闻文档进行特定于事件的爬行来创建资源。我们发布的注释语料库与必要的统计数据，并显示其使用与一个发达的系统所关注的问题。
英文摘要：
Detecting novelty of an entire document is an Artificial Intelligence (AI) frontier problem that has widespread NLP applications, such as extractive document summarization, tracking development of news events, predicting impact of scholarly articles, etc. Important though the problem is, we are unaware of any benchmark document level data that correctly addresses the evaluation of automatic novelty detection techniques in a classification framework. To bridge this gap, we present here a resource for benchmarking the techniques for document level novelty detection. We create the resource via event-specific crawling of news documents across several domains in a periodic manner. We release the annotated corpus with necessary statistics and show its use with a developed system for the problem in concern.
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。
