Abstract
The increase of information and knowledge has brought great challenge in knowledge management which includes knowledge storage, information retrieval and knowledge sharing. In digital publication domain, books are segmented into items that focus on target topic for dynamic digital publication. The management of items has great need to annotate items automatically instead of annotating by editor manually. This paper proposed probability based and hybrid method to recommend meaningful keywords for items. Experiment shows that the methods we proposed get more than 90 % precision, recall and f1 value on the digital publication dataset which outperforms the traditional extraction based and tfidf similarity based method in keyword recommendation.
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Acknowledgments
The paper is supported and completed under the financial aid of the National Science-Technology Support Plan Projects “Research and Development of Key Support Technology and Application Demonstration on Dynamic Digital Publishing” (2012BAH88F00, 2012BAH88F02).
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Li, Y., Feng, X., Zhang, S. (2015). A Practical Keyword Recommendation Method Based on Probability in Digital Publication Domain. In: Sun, M., Liu, Z., Zhang, M., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2015 2015. Lecture Notes in Computer Science(), vol 9427. Springer, Cham. https://doi.org/10.1007/978-3-319-25816-4_33
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DOI: https://doi.org/10.1007/978-3-319-25816-4_33
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