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Hybrid Neural Network for Recognition of the “de” Structure with Semantic Ellipsis
SHI Bingqing, DAI Rubing, QU Weiguang, GU Yanhui, ZHOU Junsheng, LI Bin, XU Ge, SHI Shengwang
Acta Scientiarum Naturalium Universitatis Pekinensis    2019, 55 (1): 75-83.   DOI: 10.13209/j.0479-8023.2018.058
Abstract769)   HTML    PDF(pc) (893KB)(148)       Save

To slove the classification of the “de” structure containing the usage of semantic ellipsis, a hybrid neural network is built. Firstly, the network uses a bidirectional LSTM (long short-term memory) neural network to learn more syntactic and semantic information of the “de” structure. Then, the network employs a Max-pooling
layer or GRU (gated recurrent unit) based multiple attention layers to capture features of ellipsis of the “de” structure by which the network can recognize the “de” structure containing the usage of semantic ellipsis. Experiments on CTB8.0 corpus show that the proposed approach can achieve accurate results efficiently, the F1 value is 96.67%.

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Similar Spatial Textual Objects Retrieval Strategy
GU Yanhui, WANG Daosheng, WANG Yonggen, LONG Yunfei, JIANG Suoliang, ZHOU Junsheng, QU Weiguang
Acta Scientiarum Naturalium Universitatis Pekinensis    2016, 52 (1): 120-126.   DOI: 10.13209/j.0479-8023.2016.008
Abstract846)   HTML    PDF(pc) (469KB)(1142)       Save

Based on the efficiency and effectiveness issue of traditional simiar spatial textual objects retrieval, a semantic aware strategy which can effectively and efficiently retrieve the top-k similar spatial textal objects is proposed. The efficient retrieval strategy which is based on spatial textual objects is built on a common framework of spatial object retrieval, and it can satisfy the efficiency and effectiveness issues of users. Extensive experimental evaluation demonstrates that the performance of the proposed method outperforms the state-of-the-art approach.

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Research on the Sense Guessing of Chinese Unknown Words Based on “Semantic Knowledge-base of Modern Chinese”
SHANG Fenfen, GU Yanhui, DAI Rubing, LI Bin, ZHOU Junsheng, QU Weiguang
Acta Scientiarum Naturalium Universitatis Pekinensis    2016, 52 (1): 10-16.   DOI: 10.13209/j.0479-8023.2016.009
Abstract1705)   HTML    PDF(pc) (396KB)(818)       Save

Based on the research issue of sense guessing of Chinese unknown words, different levels of semantic dictionary were introduced by applying “Semantic Knowledge-base of Modern Chinese”. Models have constructed for sense guessing by using these dictionary. Each model was intergrated to predict the unknown words and obtained better performance. Based on each model, semantic prediction and annotation of the unknown words in People’s Daily which published in 2000 were evaluated. Finally, corpus resources with the sense annotation of unknown words were obtained.

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Multi-strategies Extraction of Chinese Synonyms
SONG Wenjie,GU Yanhui,ZHOU Junsheng,SUN Yujie,YAN Jie,QU Weiguang
Acta Scientiarum Naturalium Universitatis Pekinensis   
Abstract830)      PDF(pc) (881KB)(570)       Save
Cilin and Chinese Concept Dictionary are used as dictionary resources in many NLP applications. The authors study some strategies on Chinese synonyms extraction according to key word of the infobox in Baidubaike and HTML tag of the web page in Zdic. Meanwhile, DIPRE (Dual Iterative Pattern Relation Expansion) is applied to discover high credible patterns and synonymous instances in Encyclopedia corpora. Extensive experimental evaluation demonstrates that proposed strategies outperform the NLP&CC 2012 evaluation results. A sophisticated synonym dictionary is built with manually proofreading for noun part of the Grammatical Knowledge-Base of Contemporary Chinese, which would make contributions to perfect the semantic systems of the Grammatical Knowledge-base of Contemporary Chinese.
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