Sense Prediction for Chinese OOV Based on Word Embedding and Semantic Knowledge
Wei Tingxin1,2,Bai Wenlei3,Qu Weiguang2,4()
1International College for Chinese Studies, Nanjing Normal University, Nanjing 210097, China 2School of Chinese Language and Literature, Nanjing Normal University, Nanjing 210097, China 3State Grid Nari Group Corporation, Nanjing 210003, China 4School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China
[Objective] This paper applies word embedding and word semantic knowledge to improve the sense prediction for Chinese Out Of Vocabulary (OOV). [Methods] First, we crawled webpages with OOV words. Then, we trained the Word2Vec and other embedding methods with the retrieved corpus. Finally, we improved the precision of OOV sense prediction with semantic knowledge of word formation, such as centro and pos filterings. [Results] We examined our method with datasets from the People’s Daily and found it achieved 87.5% precision on OOV sense prediction. Our result was much better than those of the models only adopting word embedding or based on semantic knowledge. [Limitations] The proposed model could not effectively predict semantically opaque OOV words. [Conclusions] Combining the external and internal information (i.e., word embedding and semantic knowledge) could remarkably improve the prediction of OOV words.
魏庭新,柏文雷,曲维光. 词向量和语义知识相结合的汉语未登录词语义预测研究*[J]. 数据分析与知识发现, 2020, 4(6): 109-117.
Wei Tingxin,Bai Wenlei,Qu Weiguang. Sense Prediction for Chinese OOV Based on Word Embedding and Semantic Knowledge. Data Analysis and Knowledge Discovery, 2020, 4(6): 109-117.
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