Keyword Extraction Based on tf/idf for Chinese News Document

Keyword extraction is an important research topic of information retrieval. This paper gave the specification of key- words in Chinese news documents based on analyzing linguistic characteristics of news documents and then proposed a new key- word extraction method based on tf/idf with multi-strategies. The approach selected candidate keywords of uni-, bi-and tri-grams, and then defines the features according to their morphological characters and context information. Moreover, the paper proposed several strategies to amend the incomplete words gotten from the word segmentation and found unknown potential keywords in news documents. Experimental results show that our proposed method can significantly outperform the baseline method. We also applied it to retrospective event detection. Experimental results show that the accuracy and efficiency of news retrospective event detection can be significantly improved.