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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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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

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.

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

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Expert Information Automatic Extraction for IOT Knowledge Base 7

Expert Information Automatic Extraction for IOT Knowledge Ba...

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7th International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Yi, Lu Yuan, Rao Long, Sun Xue, Li Xi An Jiao Tong Univ Sch Software Lab Social Intelligence & Complex Data Proc Xian 710049 Shaanxi Peoples R 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. (C) 2019 The Authors. Published by Elsevier B.V.

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

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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

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

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Relation Extraction Based on Dual Attention Mechanism

Relation Extraction Based on Dual Attention Mechanism

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2019国际计算机前沿大会

作者： Xue Li Yuan Rao Long Sun Yi Lu Lab of Social Intelligence and Complex Data Processing School of SoftwareXi'an Jiaotong University

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 *** the information processing on the sentence set is still *** at the above problems,a relation extraction method combining bidirectional GRU network and multiattention mechanism is *** 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 *** experimental verification in the NYT dataset was *** experimental results show that the proposed method can effectively improve the F1 value of the relationship extraction.

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DTCA: Decision tree-based co-Attention networks for explainable claim verification

arXiv

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arXiv 2020年

作者： Wu, Lianwei Rao, Yuan Zhao, Yongqiang Liang, Hao Nazir, Ambreen Lab of Social Intelligence and Complexity Data Processing School of Software Engineering Xi'an Jiaotong University China China Research Institute of Xi'an Jiaotong University Shenzhen China

Recently, many methods discover effective evidence from reliable sources by appropriate neural networks for explainable claim verification, which has been widely recognized. However, in these methods, the discovery process of evidence is nontransparent and unexplained. Simultaneously, the discovered evidence only roughly aims at the interpretability of the whole sequence of claims but insufficient to focus on the false parts of claims. In this paper, we propose a Decision Tree-based Co-Attention model (DTCA) to discover evidence for explainable claim verification. Specifically, we first construct Decision Tree-based Evidence model (DTE) to select comments with high credibility as evidence in a transparent and interpretable way. Then we design Co-Attention Self-Attention networks (CaSa) to make the selected evidence interact with claims, which is for 1) training DTE to determine the optimal decision thresholds and obtain more powerful evidence;and 2) utilizing the evidence to find the false parts in the claim. Experiments on two public datasets, RumourEval and PHEME, demonstrate that DTCA not only provides explanations for the results of claim verification but also achieves the state-of-The-Art performance, boosting the F1-score by 3.11%, 2.41%, respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision trees

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SLBCNN: A Improved Deep Learning Model for Few-Shot Charge Prediction

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Procedia Computer Science 2020年 174卷 32-39页

作者： Xue Li Yuan Rao Wanzhen Wang Cong Feng Research Institute of Xi’an Jiaotong University Shenzhen China Lab of Social Intelligence and Complex Data Precessiong School of Software Engineering Xi’an Jiao Tong University Xi’an China Shaanxi Joint Key Laboratory for Artifact Intelligence Xi’an Jiaotong University Xi’an China

Traditional prediction methods of legal judgment rarely work well on few-shot charge prediction task, which is intended to predict possible crimes, laws and terms according to a given few-shot case description. A major challenge in developing an efficient few-shot charge prediction algorithm is how to effectively use all available features contained in the typical cases. To this end, we proposed a novel few-shot charge prediction method based on deep learning framework, named Semi-supervised Learning with BERT-text-Convolutional neural network (i.e. SLBCNN), which utilizes the Attribute-Tri-Training method to extract the features from the few-shot case data. Experiments on three datasets of Criminal-S, Criminal-M, and Criminal-L demonstrate that SLBCNN achieves the state-of-the-art performance, boosting the F1 by 5.2%, 4.2%, and 1.5%, respectively.

关键词： few-shot charge prediction discriminative legal attributes Semi-Supervised Learning

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Joint Event Extraction Model based on Multi-feature Fusion

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Procedia Computer Science 2020年 174卷 115-122页

作者： Wang Shuo Rao Yuan Fan Xiaobing Qi Jiangnan Research Institute of Xi’an Jiaotong University Shenzhen China Lab of Social Intelligence and Complex Data Precessiong School of Software Engineering Xi’an Jiao Tong University Xi’an China Shaanxi Joint Key Laboratory for Artifact Intelligence Xi’an Jiaotong University Xi’an China

Event extraction is a challenging problem in information extraction, designed to extract structured information from unstructured text. The existing event extraction methods are mostly based on the pipeline model and use the traditional language representation model for word embedding. It is impossible to model the polysemy and the features is very simple, besides, the pipeline model is also easy to generate cascading errors. Aiming at the above problems, we propose a joint event extraction model, which combines part-of-speech features, entity features and word representation of BERT model. Because of the BERT can’t capture local features, we use multi-CNN to capture the local features of different scales. and further use BiLSTM to capture context features to assist joint event extraction. Experimental results show that the precision, recall and F 1 of the method on the CEC dataset are 88.83%, 90.91% and 89.86%, respectively, which are 1.61%, 3.6% and 2.6% higher than the PLMEE model.

关键词： Event extraction multi-feature fusion BERT

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

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

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International Joint Symposium on Artificial intelligence and Natural Language processing (iSAI-NLP)

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

ISBN: (数字)9781728156316

ISBN: (纸本)9781728156323

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.

关键词： Semantics Feature extraction Task analysis Convolution data mining Bit error rate social intelligence

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A method of Chinese named entity recognition based on CNN-BILSTM-CRF model 1

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4th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

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

ISBN: (数字)9789811322068

ISBN: (纸本)9789811322051

The main task of naming entity recognition is to identify the person names, location names, organization names, meaningful time, dates and other quantitative phrases and also classifying them into different categories. Yet, there are lots of field terms, meaningful entities, complicated location names and complex name of organizations contained in all kinds of these field terms is the most important mission in digging out the text now. The extraction of Chinese is more difficult than English entities owing to the lack of definite boundary and size characteristics of Chinese words. Therefore, we would train and test the marked corpus which is based on the CNN-BILSTM-CRF neutral network model in order to make good use of CNN to get the presentation character of words and label the words by using BILSTM and CRF. It makes the extraction of entity in the common came true. The experimental results show that the accuracy rate, recall rate and F value that we got in an unaddressed artificial features condition are 98.81%, 90.70% and 91.57% respectively which is better than the results we got by using the method of BILSTM+CRF and conditional random field (CRF). © Springer Nature Singapore Pte Ltd. 2018.

关键词： Extraction

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