Academic Journal of Computing & Information Science, 2020, 3(3); doi: 10.25236/AJCIS.2020.030309.
College of Information Engineering,Nanjing University of Finance and Economics, 210023, China
Bidding announcement widely exists in Chinese government procurement websites at all levels in the form of text. Its composition is complex and the number is numerous. Identifying and extracting more concise project names is helpful to improve the ability of website data query and analysis. To solve this problem, this paper proposes a Transformer-att-label model based on deep learning. The model uses Transformer-att for feature extraction. It uses the attention mechanism to replace the original multi-head combination of Transformer, which can improve the recognition effect. And combined with tag embedding, predict the tag semantics of words, and select the tag closest to its semantics for output. The proposed model was tested on the announcement title data set of the Chinese bidding website, and the recognition effect of other mainstream models was compared to verify the effectiveness of the method.
Chinese project name, named entity recognition, deep learning, Transformer, label
Kong Zhang. Entity recognition of Chinese bidding announcement title based on deep learning. Academic Journal of Computing & Information Science (2020), Vol. 3, Issue 3: 83-91. https://doi.org/10.25236/AJCIS.2020.030309.
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