基于TF-IDF标签集匹配的神经关键词提取方法
Extending Neural Keyword Extraction with TF-IDF tagset matching
摘要：
关键词提取是识别最能描述给定文档的单词（或多词表达）的任务，并在新闻门户网站中用于链接类似主题的文章。在这项工作中，我们开发和评估我们的方法在四个新的数据集，涵盖较少代表性，形态丰富的语言在欧洲新闻媒体行业（克罗地亚语，爱沙尼亚语，拉脱维亚语和俄语）。首先，我们对两种基于监督神经变换器的方法（TNT-KID和BERT+BiLSTM CRF）进行了评估，并将其与基于基线TF-IDF的无监督方法进行了比较。接下来，我们表明，通过结合神经Transformer为基础的方法检索的关键字和扩展的最终一组关键字与无监督的TF-IDF为基础的技术，我们可以大大提高系统的召回，使其适合作为一个推荐系统中的媒体之家的环境。
英文摘要：
Keyword extraction is the task of identifying words (or multi-word expressions) that best describe a given document and serve in news portals to link articles of similar topics. In this work we develop and evaluate our methods on four novel data sets covering less represented, morphologically-rich languages in European news media industry (Croatian, Estonian, Latvian and Russian). First, we perform evaluation of two supervised neural transformer-based methods (TNT-KID and BERT+BiLSTM CRF) and compare them to a baseline TF-IDF based unsupervised approach. Next, we show that by combining the keywords retrieved by both neural transformer based methods and extending the final set of keywords with an unsupervised TF-IDF based technique, we can drastically improve the recall of the system, making it appropriate to be used as a recommendation system in the media house environment.
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。
