Automatic Classification of Ancient Classics with Entity Features
Heran Qin1,Liu Liu1,2,Bin Li3,Dongbo Wang1,2()
1 College of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095, China 2 Research Center for Correlation of Domain Knowledge, Nanjing Agricultural University, Nanjing 210095, China 3 College of Literature, Nanjing Normal University, Nanjing 210097, China
[Objective] This paper modifies the algorithm of traditional statistical feature words with entity features, aiming to classify ten classics from ancient China. [Methods] For the support vector machine model, we added the traditional TF-IDF, information gain, chi-square test and mutual information to calculate the feature words. Then, we used the named entity to evaluate the classification results. [Results] The highest accuracy of the proposed classifier reached 98.7%. The accuracy was improved by 12.4%, 12.4%, 12.3% and 22.8% respectively with traditional information gain, TF-IDF, mutual information and chi-square test feature calculations. [Limitations] We need to re-label the recognition entities before applying entity features to other texts. [Conclusions] Entity features could improve the effectiveness of text categorization models.
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