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6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020

作者： Wang, Leipeng Rao, Yuan Bian, Qinyu Wang, Shuo Shenzhen Research Institute of Xi’an Jiaotong University Shenzhen518057 China Lab of Social Intelligence and Complex Data Processing Software School Xi’an Jiaotong University Xi’an710049 China Xi’an710049 China

ISBN: (纸本)9789811579837

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 multi-source 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. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Pre-trained Language Embedding-based Contextual Summary and Multi-scale Transmission Network for Aspect Extraction 8

Pre-trained Language Embedding-based Contextual Summary and ...

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8th International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2019

作者： Feng, Cong Rao, Yuan Nazir, Ambreen Wu, Lianwei He, Long Research Institute of Xi’an Jiaotong University Shenzhen China Lab of Social Intelligence and Complex Data Processing School of Software Engineering Xi’an Jiao Tong University Xi’an China Shaanxi Joint Key Laboratory for Artifact Intelligence Xi’an Jiao Tong University Xi’an China

With the development of IOT and 5G technology, people’s demand for information acquisition is more inclined to accuracy, intelligence and timeliness. How to help designer obtain the real-time information of specific product reviews from the massive online consumers and upgrade the new design strategy has become a hot topic for research. In this paper, we define the problem as an aspect extraction task, and propose a novel deep learning model that comprises of three modules: pre-training language model embedding, multi-scale transmission network and contextual summary, which aims to provide an end-to-end solution without any additional supervision. To this end, we adopt BERT to overcome the disadvantage of traditional embedding methods, which cannot combine contextual information. Multi-scale transmission network is proposed to integrate the Bi-GRU and a group of CNN networks to extract sequential and local features of words respectively. Contextual summary is a tailor-made representation distilled from the input sentence, conditioned on each current word, and thus can assist aspect prediction. Experimental results over three benchmark SemEval datasets clearly illustrate that our model can achieve the state-of-the-art performance. © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/)

关键词： Extraction

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Relation Extraction Based on Dual Attention Mechanism 5th

Relation Extraction Based on Dual Attention Mechanism

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5th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2019

作者： Li, Xue Rao, Yuan Sun, Long Lu, Yi Lab of Social Intelligence and Complex Data Processing School of Software Xi’an Jiaotong University Xi’an710049 China

ISBN: (纸本)9789811501173

The traditional deep learning model has problems that the long-distance 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 multi-attention 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. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Deep learning

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Semantic Enhancement and Multi-level label Embedding for Chinese News Headline Classification 14

Semantic Enhancement and Multi-level Label Embedding for Chi...

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14th International Joint Symposium on Artificial intelligence and Natural Language processing, iSAI-NLP 2019

作者： Qi, Jiangnan Rao, Yuan Sun, Ling Yang, Xiong School of Software Engineering xi'An Jiao Tong University Lab of Social Intelligence and Complex Data Processing Xi'an China

ISBN: (纸本)9781728156316

News headline classification is a specific example of short text classification, which aims to extract semantic information from the short text and classify it accurately. It can provide a fast classification method for data of various kinds of news media, thus arousing the common concern of academia and industry. Most short text classification methods are based on the semantic expansion of external knowledge, which is unable to expansion dynamically in real time and make full use of label information. To overcome these problems, we propose a novel method which consists of three parts: semantic enhancement, multi-dimensional feature fusion network and multi-level label embedding. Firstly, the word-level semantic information are embedded into the character encoding from pre-Train model to enhance semantic features. Secondly, both of Bi-GRU and multi-scale CNN are used to extract sequence and local features of text to enhance the semantic representation of the sentence. Furthermore, the multi-level label embedding is used to filter textual vector and assist classification in the word and sentence level respectively. Experimental results on NLPCC 2017 Chinese news headline classification task show that our model achieves 84.74% of accuracy and 84.75% of F1, improves over the best baseline model by 1.5% and 1.6%, respectively, and reaches the state-of-The-Art performance. © 2019 IEEE.

关键词： Semantics

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Discovering differential features: Adversarial learning for information credibility evaluation

arXiv

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arXiv 2019年

作者： Wu, Lianwei Rao, Yuan Nazir, Ambreen Jin, Haolin Lab of Social Intelligence Complexity Data Processing School of Software Engineering Xi’an Jiaotong University Xi’an710049 China

A series of deep learning approaches extract a large number of credibility features to detect fake news on the Internet. However, these extracted features still suffer from many irrelevant and noisy features that restrict severely the performance of the approaches. In this paper, we propose a novel model based on Adversarial Networks and inspirited by the Shared-Private model (ANSP), which aims at reducing common, irrelevant features from the extracted features for information credibility evaluation. Specifically, ANSP involves two tasks: one is to prevent the binary classification of true and false information for capturing common features relying on adversarial networks guided by reinforcement learning. Another extracts credibility features (henceforth, private features) from multiple types of credibility information and compares with the common features through two strategies, i.e., orthogonality constraints and KL-divergence for making the private features more differential. Experiments first on two six-label LIAR and Weibo datasets demonstrate that ANSP achieves the state-of-the-art performance, boosting the accuracy by 2.1%, 3.1%, respectively and then on four-label Twitter16 validate the robustness of the model with 1.8% performance improvements. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Different absorption from the same sharing: Sifted multi-task learning for fake news detection

arXiv

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arXiv 2019年

作者： Wu, Lianwei Rao, Yuan Jin, Haolin Nazir, Ambreen Sun, Ling Lab of Social Intelligence Complexity Data Processing School of Software Engineering Xi'an Jiaotong University Xi'an710049 China

Recently, neural networks based on multitask learning have achieved promising performance on fake news detection, which focus on learning shared features among tasks as complementary features to serve different tasks. However, in most of the existing approaches, the shared features are completely assigned to different tasks without selection, which may lead to some useless and even adverse features integrated into specific tasks. In this paper, we design a sifted multi-task learning method with a selected sharing layer for fake news detection. The selected sharing layer adopts gate mechanism and attention mechanism to filter and select shared feature flows between tasks. Experiments on two public and widely used competition datasets, i.e. RumourEval and PHEME, demonstrate that our proposed method achieves the state-of-the-art performance and boosts the F1-score by more than 0.87%, 1.31%, respectively. Copyright © 2019, The Authors. All rights reserved.

关键词： Feature extraction

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Content-Based Hybrid Deep Neural Network Citation Recommendation Method

Content-Based Hybrid Deep Neural Network Citation Recommenda...

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2020国际计算机前沿大会

作者： Leipeng Wang Yuan Rao Qinyu Bian Shuo Wang Shenzhen Research Institute of Xi’an Jiaotong University Lab of Social Intelligence and Complex Data Processing Software SchoolXi’an Jiaotong University Shanxi Joint Key Laboratory for Artifact Intelligence (Sub-Lab of Xi’an Jiaotong University)

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.

关键词： Citationrecommendation Recurrentneuralnetwork Convolutionalneuralnetwork BERT Deepsemanticmatching

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Pre-trained Language Embedding-based Contextual Summary and Multi-scale Transmission Network for Aspect Extraction

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Procedia Computer Science 2020年 174卷 40-49页

作者： Cong Feng Yuan Rao Ambreen Nazir Lianwei Wu Long He Research Institute of Xi’an Jiaotong University Shenzhen China Lab of Social Intelligence and Complex Data Processing School of Software Engineering Xi’an Jiao Tong University Xi’an China Shaanxi Joint Key Laboratory for Artifact Intelligence Xi’an Jiao Tong University Xi’an China

关键词： multi-scale transmission network pre-trained language embedding contextual summary

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Adaptive Interaction Fusion Networks for Fake News Detection

arXiv

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arXiv 2020年

作者： Wu, Lianwei Rao, Yuan Lab of Social Intelligence and Complex Data Processing Software School Xi’an Jiaotong University Xi’an China Xi’an China Research Institute of Xi’an Jiaotong University Shenzhen China

The majority of existing methods for fake news detection universally focus on learning and fusing various features for detection. However, the learning of various features is independent, which leads to a lack of cross-interaction fusion between features on social media, especially between posts and comments. Generally, in fake news, there are emotional associations and semantic conflicts between posts and comments. How to represent and fuse the cross-interaction between both is a key challenge. In this paper, we propose Adaptive Interaction Fusion Networks (AIFN) to fulfill cross-interaction fusion among features for fake news detection. In AIFN, to discover semantic conflicts, we design gated adaptive interaction networks (GAIN) to capture adaptively similar semantics and conflicting semantics between posts and comments. To establish feature associations, we devise semantic-level fusion self-attention networks (SFSN) to enhance semantic correlations and fusion among features. Extensive experiments on two real-world datasets, i.e., RumourEval and PHEME, demonstrate that AIFN achieves the state-of-the-art performance and boosts accuracy by more than 2.05% and 1.90%, respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

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Expert Information Automatic Extraction for IOT Knowledge Base

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Procedia Computer Science 2019年 147卷 288-294页

作者： Lu Yi Rao Yuan Sun Long Li Xue Lab of Social Intelligence & Complex Data Processing School of Software Xi’an Jiaotong University Xi’an 710049 China

With the rapid development of IOT technology, the requirement of effective and accurate retrieval of domain knowledge is growing. Automatically extract various information of expert from the massive web pages and generate a dynamic and wholeness profile model are important for knowledge base. However, the obvious differences in structure and content semantics of web pages between any two websites shows traditional web crawler are hard to understand the semantic of the web page and extract the critical information of expert. Therefore, a six-dimension expert profile model was introduced and then a sequence tagging method with LSTM-CRF model was proposed to automatically extract rich semantic information basing on organization structure, meaning of words and attributes of experts. The results of the experiment on test data sets illustrated that the precision rate and recall rate about the job experience and research field of experts are 67.8%, 66.6% and 82.4%, 79.6%, respectively. In addition, the overall average F value about some obvious features of expert, such as name, title, email, achievement, etc., reaches 82.5%, which is better than the results by MEMM and LSTM algorithms.

关键词： Internet of Things Expert profile model Deep Learning Sequence Tagging

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