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1.
With the information explosion of news articles, personalized news recommendation has become important for users to quickly find news that they are interested in. Existing methods on news recommendation mainly include collaborative filtering methods which rely on direct user-item interactions and content based methods which characterize the content of user reading history. Although these methods have achieved good performances, they still suffer from data sparse problem, since most of them fail to extensively exploit high-order structure information (similar users tend to read similar news articles) in news recommendation systems. In this paper, we propose to build a heterogeneous graph to explicitly model the interactions among users, news and latent topics. The incorporated topic information would help indicate a user’s interest and alleviate the sparsity of user-item interactions. Then we take advantage of graph neural networks to learn user and news representations that encode high-order structure information by propagating embeddings over the graph. The learned user embeddings with complete historic user clicks capture the users’ long-term interests. We also consider a user’s short-term interest using the recent reading history with an attention based LSTM model. Experimental results on real-world datasets show that our proposed model significantly outperforms state-of-the-art methods on news recommendation.  相似文献   

2.
Syntax parse trees are a method of representing sentence structure and are often used to provide models with syntax information and enhance downstream task performance. Because grammar and syntax are inherently linked, the incorporation of syntax parse trees in GEC is a natural solution. In this work, we present a method of incorporating syntax parse trees for Grammatical Error Correction (GEC). Building off a strong sequence-to-sequence Transformer baseline, we present a unified parse integration method for GEC that allows for the use of both dependency and constituency parse trees, as well as their combination - a syntactic graph. Specifically, on the sentence encoder, we propose a graph encoder that can encode dependency trees and constituent trees at the same time, yielding two representations for terminal nodes (i.e., the token of the sentence) and non-terminal nodes. We next use two cross-attentions (NT-Cross-Attention and T-Cross-Attention) to aggregate these source syntactic representations to the target side for final corrections prediction. In addition to evaluating our models on the popular CoNLL-2014 Shared Task and JFLEG GEC benchmarks, we affirm the effectiveness of our proposed method by testing both varying levels of parsing quality and exploring the use of both parsing formalisms. With further empirical exploration and analysis to identify the source of improvement, we found that rich syntax information provided clear clues for GEC; a syntactic graph composed of multiple syntactic parse trees can effectively compensate for the limited quality and insufficient error correction capability of a single syntactic parse tree.  相似文献   

3.
Recently, phishing scams have become one of the most serious types of crime involved in Ethereum, the second-largest blockchain-based cryptocurrency platform. The existing phishing scams detection techniques for Ethereum mostly use traditional machine learning or network representation learning to mine the key information from the transaction network and identify phishing addresses. However, these methods typically crop the temporal transaction graph into snapshot sequences or construct temporal random wanderings to model the dynamic evolution of the topology of the transaction graph. In this paper, we propose PDTGA, a method that applies graph representation learning based on temporal graphs attention to improve the effectiveness of phishing scams detection in Ethereum. Specifically, we learn the functional representation of time directly and model the time signal through the interactions between the time encoding function and node features, edge features, and the topology of the graph. We collected a real-world Ethereum phishing scam dataset, containing over 250,000 transaction records between more than 100,000 account addresses, and divided them into three datasets of different sizes. Through data analysis, we first summarized the periodic pattern of Ethereum phishing scam activities. Then we constructed 14 kinds of account node features and 3 kinds of transaction edge features. Experimental evaluations based on the above three datasets demonstrate that PDTGA with 94.78% AUC score and 88.76% recall score outperforms the state-of-the-art methods.  相似文献   

4.
Graph neural networks have been frequently applied in recommender systems due to their powerful representation abilities for irregular data. However, these methods still suffer from the difficulties such as the inflexible graph structure, sparse and highly imbalanced data, and relatively shallow networks, limiting rate prediction ability for recommendations. This paper presents a novel deep dynamic graph attention framework based on influence and preference relationship reconstruction (DGA-IPR) for recommender systems to learn optimal latent representations of users and items. The entire framework involves a user branch and an item branch. An influence-based dynamic graph attention (IDGA) module, a preference-based dynamic graph attention (PDGA) module, and an adaptive fine feature extraction (AFFE) module are respectively constructed for each branch. Concretely, the first two attention modules concentrate on reconstructing influence and preference relationship graphs, breaking imbalanced and fixed constraints of graph structures. Then a deep feature aggregation block and an adaptive feature fusion operation are built, improving the network depth and capturing potential high-order information expressions. Besides, AFFE is designed to acquire finer latent features for users and items. The DGA-IPR architecture is formed by integrating IDGA, PDGA, and AFFE for users and items, respectively. Experiments reveal the superiority of DGA-IPR over existing recommendation models.  相似文献   

5.
In event-based social networks (EBSN), group event recommendation has become an important task for groups to quickly find events that they are interested in. Existing methods on group event recommendation either consider just one type of information, explicit or implicit, or separately model the explicit and implicit information. However, these methods often generate a problem of data sparsity or of model vector redundancy. In this paper, we present a Graph Multi-head Attention Network (GMAN) model for group event recommendation that integrates the explicit and implicit information in EBSN. Specifically, we first construct a user-explicit graph based on the user's explicit information, such as gender, age, occupation and the interactions between users and events. Then we build a user-implicit graph based on the user's implicit information, such as friend relationships. The incorporated both explicit and implicit information can effectively describe the user's interests and alleviate the data sparsity problem. Considering that there may be a correlation between the user's explicit and implicit information in EBSN, we take the user's explicit vector representation as the input of the implicit information aggregation when modeling with graph neural networks. This unified user modeling can solve the aforementioned problem of user model vector redundancy and is also suitable for event modeling. Furthermore, we utilize a multi-head attention network to learn richer implicit information vectors of users and events from multiple perspectives. Finally, in order to get a higher level of group vector representation, we use a vanilla attention mechanism to fuse different user vectors in the group. Through experimenting on two real-world Meetup datasets, we demonstrate that GMAN model consistently outperforms state-of-the-art methods on group event recommendation.  相似文献   

6.
Extracting semantic relationships between entities from text documents is challenging in information extraction and important for deep information processing and management. This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based relation extraction using support vector machines. Our study illustrates that the base phrase chunking information is very effective for relation extraction and contributes to most of the performance improvement from syntactic aspect while current commonly used features from full parsing give limited further enhancement. This suggests that most of useful information in full parse trees for relation extraction is shallow and can be captured by chunking. This indicates that a cheap and robust solution in relation extraction can be achieved without decreasing too much in performance. We also demonstrate how semantic information such as WordNet, can be used in feature-based relation extraction to further improve the performance. Evaluation on the ACE benchmark corpora shows that effective incorporation of diverse features enables our system outperform previously best-reported systems. It also shows that our feature-based system significantly outperforms tree kernel-based systems. This suggests that current tree kernels fail to effectively explore structured syntactic information in relation extraction.  相似文献   

7.
Extracting semantic relationships between entities from text documents is challenging in information extraction and important for deep information processing and management. This paper proposes to use the convolution kernel over parse trees together with support vector machines to model syntactic structured information for relation extraction. Compared with linear kernels, tree kernels can effectively explore implicitly huge syntactic structured features embedded in a parse tree. Our study reveals that the syntactic structured features embedded in a parse tree are very effective in relation extraction and can be well captured by the convolution tree kernel. Evaluation on the ACE benchmark corpora shows that using the convolution tree kernel only can achieve comparable performance with previous best-reported feature-based methods. It also shows that our method significantly outperforms previous two dependency tree kernels for relation extraction. Moreover, this paper proposes a composite kernel for relation extraction by combining the convolution tree kernel with a simple linear kernel. Our study reveals that the composite kernel can effectively capture both flat and structured features without extensive feature engineering, and easily scale to include more features. Evaluation on the ACE benchmark corpora shows that the composite kernel outperforms previous best-reported methods in relation extraction.  相似文献   

8.
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9.
The proliferation of false information is a growing problem in today's dynamic online environment. This phenomenon requires automated detection of fake news to reduce its harmful effect on society. Even though various methods are used to detect fake news, most methods only consider data-oriented text features; ignoring dual emotion features (publisher emotions and social emotions) and thus lack higher levels of accuracy. This study addresses this issue by utilizing dual emotion features to detect fake news. The study proposes a Deep Normalized Attention-based mechanism for enriched extraction of dual emotion features and an Adaptive Genetic Weight Update-Random Forest (AGWu-RF) for classification. First, the deep normalized attention-based mechanism incorporates BiGRU, which improves feature value by extracting long-range context information to eliminate gradient explosion issues. The genetic weight for the model is adjusted to RF and updated to achieve optimized hyper parameter values ​​that support the classifiers' detection accuracy. The proposed model outperforms baseline methods on standard benchmark metrics in three real-world datasets. It outperforms state-of-the-art approaches by 5%, 11%, and 14% in terms of accuracy, highlighting the significance of dual emotion capabilities and optimizations in improving fake news detection.  相似文献   

10.
Existing approaches to learning path recommendation for online learning communities mainly rely on the individual characteristics of users or the historical records of their learning processes, but pay less attention to the semantics of users’ postings and the context. To facilitate the knowledge understanding and personalized learning of users in online learning communities, it is necessary to conduct a fine-grained analysis of user data to capture their dynamical learning characteristics and potential knowledge levels, so as to recommend appropriate learning paths. In this paper, we propose a fine-grained and multi-context-aware learning path recommendation model for online learning communities based on a knowledge graph. First, we design a multidimensional knowledge graph to solve the problem of monotonous and incomplete entity information presentation of the single layer knowledge graph. Second, we use the topic preference features of users’ postings to determine the starting point of learning paths. We then strengthen the distant relationship of knowledge in the global context using the multidimensional knowledge graph when generating and recommending learning paths. Finally, we build a user background similarity matrix to establish user connections in the local context to recommend users with similar knowledge levels and learning preferences and synchronize their subsequent postings. Experiment results show that the proposed model can recommend appropriate learning paths for users, and the recommended similar users and postings are effective.  相似文献   

11.
How to parse the human image to obtain the text label corresponding to the human body is a critical task for human-computer interaction. Although previous methods have significantly improved the parsing performance, the problem of parsing confusion and tiny target missing remains unresolved, which leads to errors and incomplete inference accordingly. Targeting at these drawbacks, we fuse semantic and spatial features to mine the human body information based on the Dual Pyramid Unit convolutional neural network, named as DPUNet. DPUNet is composed of Context Pyramid Unit (CPU) and Spatial Pyramid Unit (SPU). Firstly, we design the CPU to aggregate the local to global semantic information, which exports the semantic feature for eliminating the semantic confusion. To capture the tiny targets for preventing the details from missing, the SPU is proposed to incorporate the multi-scale spatial information and output the spatial feature. Finally, the features of two complementary units are fused for accurate and complete human parsing results. Our approach achieves more excellent performance than the state-of-the-art methods on single human and multiple human parsing datasets. Meanwhile, the proposed framework is efficient with a fast speed of 41.2fps.  相似文献   

12.
Within the context of Information Extraction (IE), relation extraction is oriented towards identifying a variety of relation phrases and their arguments in arbitrary sentences. In this paper, we present a clause-based framework for information extraction in textual documents. Our framework focuses on two important challenges in information extraction: 1) Open Information Extraction and (OIE), and 2) Relation Extraction (RE). In the plethora of research that focus on the use of syntactic and dependency parsing for the purposes of detecting relations, there has been increasing evidence of incoherent and uninformative extractions. The extracted relations may even be erroneous at times and fail to provide a meaningful interpretation. In our work, we use the English clause structure and clause types in an effort to generate propositions that can be deemed as extractable relations. Moreover, we propose refinements to the grammatical structure of syntactic and dependency parsing that help reduce the number of incoherent and uninformative extractions from clauses. In our experiments both in the open information extraction and relation extraction domains, we carefully evaluate our system on various benchmark datasets and compare the performance of our work against existing state-of-the-art information extraction systems. Our work shows improved performance compared to the state-of-the-art techniques.  相似文献   

13.
Multimodal fake news detection methods based on semantic information have achieved great success. However, these methods only exploit the deep features of multimodal information, which leads to a large loss of valid information at the shallow level. To address this problem, we propose a progressive fusion network (MPFN) for multimodal disinformation detection, which captures the representational information of each modality at different levels and achieves fusion between modalities at the same level and at different levels by means of a mixer to establish a strong connection between the modalities. Specifically, we use a transformer structure, which is effective in computer vision tasks, as a visual feature extractor to gradually sample features at different levels and combine features obtained from a text feature extractor and image frequency domain information at different levels for fine-grained modeling. In addition, we design a feature fusion approach to better establish connections between modalities, which can further improve the performance and thus surpass other network structures in the literature. We conducted extensive experiments on two real datasets, Weibo and Twitter, where our method achieved 83.3% accuracy on the Twitter dataset, which has increased by at least 4.3% compared to other state-of-the-art methods. This demonstrates the effectiveness of MPFN for identifying fake news, and the method reaches a relatively advanced level by combining different levels of information from each modality and a powerful modality fusion method.  相似文献   

14.
Abnormal event detection in videos plays an essential role for public security. However, most weakly supervised learning methods ignore the relationship between the complicated spatial correlations and the dynamical trends of temporal pattern in video data. In this paper, we provide a new perspective, i.e., spatial similarity and temporal consistency are adopted to construct Spatio-Temporal Graph-based CNNs (STGCNs). For the feature extraction, we use Inflated 3D (I3D) convolutional networks to extract features which can better capture appearance and motion dynamics in videos. For the spatio graph and temporal graph, each video segment is regarded as a vertex in the graph, and attention mechanism is introduced to allocate attention for each segment. For the spatial-temporal fusion graph, we propose a self-adapting weighting to fuse them. Finally, we build ranking loss and classification loss to improve the robustness of STGCNs. We evaluate the performance of STGCNs on UCF-Crime datasets (total 128 h) and ShanghaiTech datasets (total 317,398 frames) with the AUC score 84.2% and 92.3%, respectively. The experimental results also show the effectiveness and robustness with other evaluation metrics.  相似文献   

15.
The wide spread of fake news and its negative impacts on society has attracted a lot of attention to fake news detection. In existing fake news detection methods, particular attention has been paid to the credibility of the users sharing the news on social media, and the news sources based on their level of participation in fake news dissemination. However, these methods have ignored the important role of news topical perspectives (like political viewpoint) in users'/sources' decisions to share/publish the news. These decisions are associated with the viewpoints shared by the echo-chamber that the users belong to, i.e., users' Socio-Cognitive (SC) biases, and the news sources' partisan bias. Therefore, the credibility of users and news sources are varied in different topics according to the mentioned biases; which are completely ignored in current fake news detection studies. In this paper, we propose a Multi-View Co-Attention Network (MVCAN) that jointly models the latent topic-specific credibility of users and news sources for fake news detection. The key idea is to represent news articles, users, and news sources in a way that the topical viewpoints of news articles, SC biases of users which determines the users' viewpoints in sharing news, and the partisan bias of news sources are encoded as vectors. Then a novel variant of the Multi-Head Co-Attention (MHCA) mechanism is proposed to encode the joint interaction from different views, including news-source and news-user to implicitly model the credibility of users and the news sources based on their interaction in real and fake news spreading on the news topic. We conduct extensive experiments on two public datasets. The results show that MVCAN significantly outperforms other state-of-the-art methods and outperforms the best baselines by 3% on average in terms of F1 and Accuracy.  相似文献   

16.
In recent years, fake news detection has been a significant task attracting much attention. However, most current approaches utilize the features from a single modality, such as text or image, while the comprehensive fusion between features of different modalities has been ignored. To deal with the above problem, we propose a novel model named Bidirectional Cross-Modal Fusion (BCMF), which comprehensively integrates the textual and visual representations in a bidirectional manner. Specifically, the proposed model is decomposed into four submodules, i.e., the input embedding, the image2text fusion, the text2image fusion, and the prediction module. We conduct intensive experiments on four real-world datasets, i.e., Weibo, Twitter, Politi, and Gossip. The results show 2.2, 2.5, 4.9, and 3.1 percentage points of improvements in classification accuracy compared to the state-of-the-art methods on Weibo, Twitter, Politi, and Gossip, respectively. The experimental results suggest that the proposed model could better capture integrated information of different modalities and has high generalizability among different datasets. Further experiments suggest that the bidirectional fusions, the number of multi-attention heads, and the aggregating function could impact the performance of the cross-modal fake news detection. The research sheds light on the role of bidirectional cross-modal fusion in leveraging multi-modal information to improve the effect of fake news detection.  相似文献   

17.
Narratives are comprised of stories that provide insight into social processes. To facilitate the analysis of narratives in a more efficient manner, natural language processing (NLP) methods have been employed in order to automatically extract information from textual sources, e.g., newspaper articles. Existing work on automatic narrative extraction, however, has ignored the nested character of narratives. In this work, we argue that a narrative may contain multiple accounts given by different actors. Each individual account provides insight into the beliefs and desires underpinning an actor’s actions. We present a pipeline for automatically extracting accounts, consisting of NLP methods for: (1) named entity recognition, (2) event extraction, and (3) attribution extraction. Machine learning-based models for named entity recognition were trained based on a state-of-the-art neural network architecture for sequence labelling. For event extraction, we developed a hybrid approach combining the use of semantic role labelling tools, the FrameNet repository of semantic frames, and a lexicon of event nouns. Meanwhile, attribution extraction was addressed with the aid of a dependency parser and Levin’s verb classes. To facilitate the development and evaluation of these methods, we constructed a new corpus of news articles, in which named entities, events and attributions have been manually marked up following a novel annotation scheme that covers over 20 event types relating to socio-economic phenomena. Evaluation results show that relative to a baseline method underpinned solely by semantic role labelling tools, our event extraction approach optimises recall by 12.22–14.20 percentage points (reaching as high as 92.60% on one data set). Meanwhile, the use of Levin’s verb classes in attribution extraction obtains optimal performance in terms of F-score, outperforming a baseline method by 7.64–11.96 percentage points. Our proposed approach was applied on news articles focused on industrial regeneration cases. This facilitated the generation of accounts of events that are attributed to specific actors.  相似文献   

18.
Talent recruitment has become a crucial issue for companies since finding suitable candidates from the massive data on potential candidates from online talent platforms is a challenging task. However, extant studies mainly focus on the scalability and inference ability of models, while the dynamic variability of the importance of each feature in different scenarios is barely addressed. Besides, there is a lack of research on how to depict the hidden potential preference of the job which cannot be derived from job requirements. In this paper, we propose a two-stage resume recommendation model based on deep learning and attention mechanisms, especially considering the latent preference information hidden in the hired employee resumes, named the Attentive Implicit Relationship-Aware Neural Network (AIRANN) model. Specifically, a novel mechanism is proposed herein to extract the hidden potential preference of the corresponding job by deriving the implicit relationship between the target resume and hired employees’ resumes. Existing studies have not considered such resume implicit relationships. Moreover, we propose a Feature Co-Attention mechanism to capture the dynamic interactive importance within the non-text features of both resumes and jobs. For different jobs, the suitability of resumes would be valued from different aspects, including resume implicit relationships, as well as textual and non-textual features. Accordingly, an Aspect-attention mechanism is designed herein to automatically adjust the variant importance of each aspect. Finally, extensive experiments are conducted on a real-world company dataset. The experiment results of ablation studies demonstrate the effectiveness of each mechanism in the proposed AIRANN model. The experiment results also show that the proposed AIRANN model outperforms other baseline methods, showing a general improvement of 13.31%, 12.49%, 6.5% and 7.17% over the state-of-the-art baseline under F1@6, F1@15, NDCG@6 and NDCG@15, respectively.  相似文献   

19.
Previous studies on Course Recommendation (CR) mainly focus on investigating the sequential relationships among courses (RNN is applied) and fail to learn the similarity relationships among learners. Moreover, existing RNN-based methods can only model courses’ short-term sequential patterns due to the inherent shortcomings of RNNs. In light of the above issues, we develop a hyperedge-based graph neural network, namely HGNN, for CR. Specifically, (1) to model the relationships among learners, we treat learners (i.e., hyperedges) as the sets of courses in a hypergraph, and convert the task of learning learners’ representations to induce the embeddings for hyperedges, where a hyperedge-based graph attention network is further proposed. (2) To simultaneously consider courses’ long-term and short-term sequential relationships, we first construct a course sequential graph across learners, and learn courses’ representations via a modified graph attention network. Then, we feed the learned representations into a GRU-based sequence encoder to infer their short-term patterns, and deem the last hidden state as the learned sequence-level learner embedding. After that, we obtain the learners’ final representations by a product pooling operation to retain features from different latent spaces, and optimize a cross-entropy loss to make recommendations. To evaluate our proposed solution HGNN, we conduct extensive experiments on two real-world datasets, XuetangX and MovieLens. We conduct experiments on MovieLens to prove the extensibility of our solution on other collections. From the experimental results, we can find that HGNN evidently outperforms other recent CR methods on both datasets, achieving 11.96% on P@20, 16.01% on NDCG@20, and 27.62% on MRR@20 on XuetangX, demonstrating the effectiveness of studying CR in a hypergraph, and the importance of considering both long-term and short-term sequential patterns of courses.  相似文献   

20.
Identifying petition expectation for government response plays an important role in government administrative service. Although some petition platforms allow citizens to label the petition expectation when they submit e-petitions, the misunderstanding and misselection of petition labels still has necessitated manual classification involved. Automatic petition expectation identification has faced challenges in poor context information, heavy noise and casual syntactic structure of the petition text. In this paper we propose a novel deep reinforcement learning based method for petition expectation (citizens’ demands for the level of government response) correction and identification named PecidRL. We collect a dataset from Message Board for Leaders, the largest official petition platform in China, containing 237,042 petitions. Firstly, we introduce a deep reinforcement learning framework to automatically correct the mislabeled and ambiguous labels of the petitions. Then, multi-view textual features, including word-level and document-level semantic features, sentiment features and different textual graph representations are extracted and integrated to enrich more auxiliary information. Furthermore, based on the corrected petitions, 19 novel petition expectation identification models are constructed by extending 11 popular machine learning models for petition expectation detection. Finally, comprehensive comparison and evaluation are conducted to select the final petition expectation identification model with the best performance. After performing correction by PecidRL, each metric on all extended petition expectation identification models improves by an average of 8.3% with the highest increase ratio reaching 14.2%. The optimal model is determined as Peti-SVM-bert with the highest accuracy 93.66%. We also analyze the petition expectation label variation of the dataset by using PecidRL. We derive that 16.9% of e-petitioners tend to exaggerate the urgency of their petitions to make the government pay high attention to their appeals and 4.4% of the petitions urgency are underestimated. This study has substantial academic and practical value in improving government efficiency. Additionally, a web-server is developed to facilitate government administrators and other researchers, which can be accessed at http://www.csbg-jlu.info/PecidRL/.  相似文献   

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