The traditional deep learning model has problems that the longdistance dependent information cannot be learned, and the correlation between the input and output of the model is not considered. And the information proc...The traditional deep learning model has problems that the longdistance dependent information cannot be learned, and the correlation between the input and output of the model is not considered. And the information processing on the sentence set is still insufficient. Aiming at the above problems, a relation extraction method combining bidirectional GRU network and multiattention mechanism is proposed. The word-level attention mechanism was used to extract the word-level features from the sentence, and the sentence-level attention mechanism was used to focus on the characteristics of sentence sets. The experimental verification in the NYT dataset was conducted. The experimental results show that the proposed method can effectively improve the F1 value of the relationship extraction.展开更多
The rapid growth of scientific papers makes it difficult to query related papers efficiently,accurately and with high coverage.Traditional citation recommendation algorithms rely heavily on the metadata of query docum...The rapid growth of scientific papers makes it difficult to query related papers efficiently,accurately and with high coverage.Traditional citation recommendation algorithms rely heavily on the metadata of query documents,which leads to the low quality of recommendation results.In this paper,DeepCite,a content-based hybrid neural network citation recommendation method is proposed.First,the BERT model was used to extract the high-level semantic representation vectors in the text,then the multi-scale CNN model and BiLSTM model were used to obtain the local information and the sequence information of the context in the sentence,and the text vectors were matched in depth to generate candidate sets.Further,the depth neural network was used to rerank the candidate sets by combining the score of candidate sets and multisource features.In the reranking stage,a variety of Metapath features were extracted from the citation network,and added to the deep neural network to learn,and the ranking of recommendation results were optimized.Compared with PWFC,ClusCite,BM25,RW,NNRank models,the results of the Deepcite algorithm presented in the ANN datasets show that the precision(P@20),recall rate(R@20),MRR and MAP indexesrise by 2.3%,3.9%,2.4%and 2.1%respectively.Experimental results on DBLP datasets show that the improvement is 2.4%,4.3%,1.8%and 1.2%respectively.Therefore,the algorithm proposed in this paper effectively improves the quality of citation recommendation.展开更多
文摘The traditional deep learning model has problems that the longdistance dependent information cannot be learned, and the correlation between the input and output of the model is not considered. And the information processing on the sentence set is still insufficient. Aiming at the above problems, a relation extraction method combining bidirectional GRU network and multiattention mechanism is proposed. The word-level attention mechanism was used to extract the word-level features from the sentence, and the sentence-level attention mechanism was used to focus on the characteristics of sentence sets. The experimental verification in the NYT dataset was conducted. The experimental results show that the proposed method can effectively improve the F1 value of the relationship extraction.
基金“Shenzhen Science and Technology Project”(JCYJ20180306170836595)“National key research and development program in China”(2019YFB2102300)+4 种基金“the World-Class Universities(Disciplines)and the Characteristic Development Guidance Funds for the Central Universities of China”(PY3A022)“Ministry of Education Fund Projects”(No.18JZD022 and 2017B00030)“Basic Scientific Research Operating Expenses of Central Universities”(No.ZDYF2017006)“Xi’an Navinfo Corp.&Engineering Center of Xi’an Intelligence Spatial-temporal Data Analysis Project”(C2020103)“Beilin District of Xi’an Science&Technology Project”(GX1803).
文摘The rapid growth of scientific papers makes it difficult to query related papers efficiently,accurately and with high coverage.Traditional citation recommendation algorithms rely heavily on the metadata of query documents,which leads to the low quality of recommendation results.In this paper,DeepCite,a content-based hybrid neural network citation recommendation method is proposed.First,the BERT model was used to extract the high-level semantic representation vectors in the text,then the multi-scale CNN model and BiLSTM model were used to obtain the local information and the sequence information of the context in the sentence,and the text vectors were matched in depth to generate candidate sets.Further,the depth neural network was used to rerank the candidate sets by combining the score of candidate sets and multisource features.In the reranking stage,a variety of Metapath features were extracted from the citation network,and added to the deep neural network to learn,and the ranking of recommendation results were optimized.Compared with PWFC,ClusCite,BM25,RW,NNRank models,the results of the Deepcite algorithm presented in the ANN datasets show that the precision(P@20),recall rate(R@20),MRR and MAP indexesrise by 2.3%,3.9%,2.4%and 2.1%respectively.Experimental results on DBLP datasets show that the improvement is 2.4%,4.3%,1.8%and 1.2%respectively.Therefore,the algorithm proposed in this paper effectively improves the quality of citation recommendation.
