Abstract
In natural language processing, the use of chunk analysis instead of parsing can greatly reduce the complexity of parsing. Noun phrase chunks, as one of the chunks, exist in a large number of sentences and play important syntactic roles such as subject and object. Therefore, it is very important to achieve high-performance recognition of noun phrase chunks for syntactic analysis. This paper presents a Vietnamese noun phrase block recognition method based on BiLSTM-CRF model and constraint rules. This method first carries out part-of-speech tagging, and integrates the marked part-of-speech features into the input vector of the model in the form of splicing. Secondly, the constraints rules are obtained by analyzing the Vietnamese noun phrase blocks. Finally, the constraints rules are integrated into the output layer of the model to realize the further optimization of the model. The experimental results show that the accuracy, recall and F-value of the method are 88.08%, 88.73% and 88.40% respectively.
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Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant Nos. 61866019, 61972186, 61732005, 61672271, 61761026, 61762056), National Key Research and Development Plan (Grant Nos. 2018YFC0830105, 2018YFC0830101, 2018YFC0830100), Science and Technology Leading Talents in Yunnan, and Yunnan High and New Technology Industry Project (Grant No. 201606), Natural Science Foundation of Yunnan Province (Grant No. 2018FB104), and Talent Fund for Kunming University of Science and Technology (Grant No. KKSY201703005).
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Lai, H., Zhao, C., Yu, Z., Gao, S., Xu, Y. (2019). Vietnamese Noun Phrase Chunking Based on BiLSTM-CRF Model and Constraint Rules. In: Jin, H., Lin, X., Cheng, X., Shi, X., Xiao, N., Huang, Y. (eds) Big Data. BigData 2019. Communications in Computer and Information Science, vol 1120. Springer, Singapore. https://doi.org/10.1007/978-981-15-1899-7_7
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