Event-Oriented Keyphrase Extraction Based on Bi-clustering Model

Lin Zhao, Liangjun Zang, Longtao Huang, Jizhong Han, Songlin Hu

Abstract: Keyphrase extraction, as a basis for many natural language processing and information retrieval tasks, can help people efficiently discover their interested information from vast streams of online documents. Previous methods are mostly proposed in general purpose, where keyphrases that represent the main topics are extracted. However, such keyphrases can hardly distinguish events from massive streams of long text documents that share similar topics and contain highly redundant information. In this paper, we address the task of keyphrase extraction for event-oriented retrieval. We propose a novel bi-clustering model for clustering the documents and keyphrases simultaneously. The model consequently makes the extracted keyphrases more specific and related to the event. We conduct a series of experiments on a real-world dataset. The experimental results demonstrate the better performance of our approach than other unsupervised approaches.