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| HRTNSC: Hybrid Representation-based Subjective and Objective Sentence Classification for Tibetan News |
| KONG Chunwei1,2 , LYU Xueqiang1,2, ZHANG Le2 |
1.School of Computer Science, Qinghai Normal University, Xi'ning, Qinghai 810008, China;
2.Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing 100101, China |
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Abstract Focused on Tibetan news texts, this paper proposes a hybrid representation-based subjective and objective sentence classification model (HRTNSC). The input layer is enriched by fusing syllable-level features and word-level features. The BiLSTM+CNN network is applied to the subjective and objective classification of sentences. The experimental results show that the HRTNSC model achieves an optimal F1 value of 90.84%, which is better than the benchmark model.
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Received: 19 January 2022
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2022, Vol. 36 
