Abstract
Keywords are subset of words or phrases from a document that can describe the meaning of the document. Many text mining applications can take advantage from it. Unfortunately, a large portion of documents still do not have keywords assigned. On the other hand, manual assignment of high quality keywords is time-consuming, and error prone. Therefore, most algorithms and systems aimed to help people perform automatic keywords extraction have been proposed. However, most methods of automatic keyword extraction cannot use the features of documents effectively. A method which integrates the statistical machine learning models is proposed in this paper. This method extracts keyword from Chinese documents through voting of multiple keywords extraction models. Experimental results show that the proposed method based on ensemble leaning outperforms other methods according to F1 measurement. Moreover, the keywords extraction model based on ensemble learning with the weighted voting outperforms the model without the weighted voting.
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Zhang, C. (2009). Combining Statistical Machine Learning Models to Extract Keywords from Chinese Documents. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2009. Lecture Notes in Computer Science(), vol 5678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03348-3_79
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DOI: https://doi.org/10.1007/978-3-642-03348-3_79
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