Keyphrase extraction is the task of extracting the most important phrases from a document. Automatic keyphrase extraction attempts to itemize a document content as metainformation and facilitate efficient information retrieval. In this paper we propose TOP-Rank, an approach for keyphrase extraction and keyphrase classification. For keyphrase extraction, we build an approach based on the position of keyphrases in the document and expand it with topical ranking of keyphrases. In particular, keyphrase extraction technique analyzes the documents and extracts keyphrases from the document by giving a higher rank to topical phrases. After keyphrase extraction, we classify keyphrases as process, material and task. Our evaluation on diverse datasets shows that TOP-Rank achieves F1-score of 0.73 for keyphrase classification improving upon state-of-the-art methods by a huge margin.

关键字短语提取是从文档中提取最重要的短语的任务。自动关键短语提取尝试将文档内容逐项列出为元信息，并促进有效的信息检索。在本文中，我们提出了TOP-Rank，一种用于关键字短语提取和关键字短语分类的方法。对于关键字短语提取，我们基于关键字短语在文档中的位置构建一种方法，并通过关键字短语的主题排名对其进行扩展。尤其是，关键短语提取技术通过对主题短语给予更高的排名来分析文档并从文档中提取关键短语。提取关键短语后，我们将关键短语分类为过程，材料和任务。我们对各种数据集的评估表明，对于最先进的方法，对于关键短语分类，TOP-Rank的F1得分达到0.73。