论文标题
通过数据增强和信息过滤改善键形提取
Improving Keyphrase Extraction with Data Augmentation and Information Filtering
论文作者
论文摘要
键形提取是NLP中文档理解的重要任务之一。虽然大多数先前的作品都致力于正式设置，例如书籍，新闻或网络博客，但较少探索了视频成绩单等非正式文本。为了解决这一局限性，在这项工作中，我们提出了一种新颖的语料库和方法，用于从Behance平台上流式传输的视频的成绩单中提取键形的方法。更具体地说，在这项工作中，提出了一种新型的数据增强，以通过背景知识从其他域中提取任务来丰富模型。提出的数据集数据集上的广泛实验显示了引入方法的有效性。
Keyphrase extraction is one of the essential tasks for document understanding in NLP. While the majority of the prior works are dedicated to the formal setting, e.g., books, news or web-blogs, informal texts such as video transcripts are less explored. To address this limitation, in this work we present a novel corpus and method for keyphrase extraction from the transcripts of the videos streamed on the Behance platform. More specifically, in this work, a novel data augmentation is proposed to enrich the model with the background knowledge about the keyphrase extraction task from other domains. Extensive experiments on the proposed dataset dataset show the effectiveness of the introduced method.
