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  • 1.
    Tran, Son N.
    et al.
    The Australian E-Health Research Centre, CSIRO, Brisbane, QLD 4026, Australia.
    Zhang, Qing
    Nguyen, Anthony
    Vu, Xuan-Son
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Ngo, Son
    Improving Recurrent Neural Networks with Predictive Propagation for Sequence Labelling2018Ingår i: Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part I, Springer, 2018, s. 452-462Konferensbidrag (Refereegranskat)
    Abstract [en]

    Recurrent neural networks (RNNs) is a useful tool for sequence labelling tasks in natural language processing. Although in practice RNNs suffer a problem of vanishing/exploding gradient, their compactness still offers efficiency and make them less prone to overfitting. In this paper we show that by propagating the prediction of previous labels we can improve the performance of RNNs while keeping the number of parameters in RNNs unchanged and adding only one more step for inference. As a result, the models are still more compact and efficient than other models with complex memory gates. In the experiment, we evaluate the idea on optical character recognition and Chunking which achieve promising results.

  • 2.
    Vu, Thanh
    et al.
    Newcastle University.
    Nguyen, Dat Quoc
    The University of Melbourne.
    Vu, Xuan-Son
    Umeå universitet.
    Nguyen, Dai Quoc
    Deakin University.
    Catt, Michael
    Newcastle University.
    Trenell, Michael
    Newcastle University.
    NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter2018Ingår i: Proceedings of The 12th International Workshop on Semantic Evaluation, New Orleans, Louisiana, USA: The Association for Computational Linguistics , 2018Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper describes our NIHRIO system for SemEval-2018 Task 3 "Irony detection in English tweets". We propose to use a simple neural network architecture of Multilayer Perceptron with various types of input features including: lexical, syntactic, semantic and polarity features.  Our system achieves very high performance in both subtasks of binary and multi-class irony detection in tweets. In particular, we rank at fifth in terms of the accuracy metric and the F1 metric. Our code is available at: https://github.com/NIHRIO/IronyDetectionInTwitter

  • 3.
    Vu, Xuan-Son
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Addi, Ait-Mlouk
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Elmroth, Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Lili, Jiang
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Graph-based Interactive Data Federation System for Heterogeneous Data Retrieval and Analytics2019Ingår i: Proceedings of The 30th TheWebConf'19 (formerly WWW), USA, New York, NY, USA: ACM Digital Library, 2019, s. 3595-3599Konferensbidrag (Refereegranskat)
    Abstract [en]

    Given the increasing number of heterogeneous data stored in relational databases, file systems or cloud environment, it needs to be easily accessed and semantically connected for further data analytic. The potential of data federation is largely untapped, this paper presents an interactive data federation system (https://vimeo.com/ 319473546) by applying large-scale techniques including heterogeneous data federation, natural language processing, association rules and semantic web to perform data retrieval and analytics on social network data. The system first creates a Virtual Database (VDB) to virtually integrate data from multiple data sources. Next, a RDF generator is built to unify data, together with SPARQL queries, to support semantic data search over the processed text data by natural language processing (NLP). Association rule analysis is used to discover the patterns and recognize the most important co-occurrences of variables from multiple data sources. The system demonstrates how it facilitates interactive data analytic towards different application scenarios (e.g., sentiment analysis, privacyconcern analysis, community detection).

  • 4.
    Vu, Xuan-Son
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Jiang, Lili
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Brändström, Anders
    Umeå universitet, Samhällsvetenskapliga fakulteten, Enheten för demografi och åldrandeforskning (CEDAR).
    Elmroth, Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Personality-based Knowledge Extraction for Privacy-preserving Data Analysis2017Ingår i: K-CAP 2017 - Proceedings of the Knowledge Capture Conference, Austin, TX, USA: ACM Digital Library, 2017, artikel-id 45Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, we present a differential privacy preserving approach, which extracts personality-based knowledge to serve privacy guarantee data analysis on personal sensitive data. Based on the approach, we further implement an end-to-end privacy guarantee system, KaPPA, to provide researchers iterative data analysis on sensitive data. The key challenge for differential privacy is determining a reasonable amount of privacy budget to balance privacy preserving and data utility. Most of the previous work applies unified privacy budget to all individual data, which leads to insufficient privacy protection for some individuals while over-protecting others. In KaPPA, the proposed personality-based privacy preserving approach automatically calculates privacy budget for each individual. Our experimental evaluations show a significant trade-off of sufficient privacy protection and data utility.

  • 5.
    Vu, Xuan-Son
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Lili, Jiang
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Self-adaptive Privacy Concern Detection for User-generated Content2018Ingår i: 19th International Conference on Computational Linguistics and Intelligent Text Processing, Hanoi, Vietnam, 2018Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    To protect user privacy in data analysis, a state-of-the-art strategy is differential privacy in which scientific noise is injected into the real analysis output. The noise masks individual’s sensitive information contained in the dataset. However, determining the amount of noise is a key challenge, since too much noise will destroy data utility while too little noise will increase privacy risk. Though previous research works have designed some mechanisms to protect data privacy in different scenarios, most of the existing studies assume uniform privacy concerns for all individuals. Consequently, putting an equal amount of noise to all individuals leads to insufficient privacy protection for some users, while over-protecting others. To address this issue, we propose a self-adaptive approach for privacy concern detection based on user personality. Our experimental studies demonstrate the effectiveness to address a suitable personalized privacy protection for cold-start users (i.e., without their privacy-concern information in training data).

  • 6.
    Vu, Xuan-Son
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Tran N., Son
    Lili, Jiang
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    dpUGC: Learn Differentially Private Representation for User Generated Contents2019Ingår i: Proceedings of the 20th International Conference on Computational Linguistics and Intelligent Text Processing, 2019Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper firstly proposes a simple yet efficient generalized approach to apply differential privacy to text representation (i.e., word embedding). Based on it, we propose a user-level approach to learn personalized differentially private word embedding model on user generated contents (UGC). To our best knowledge, this is the first work of learning user-level differentially private word embedding model from text for sharing. The proposed approaches protect the privacy of the individual from re-identification, especially provide better trade-off of privacy and data utility on UGC data for sharing. The experimental results show that the trained embedding models are applicable for the classic text analysis tasks (e.g., regression). Moreover, the proposed approaches of learning differentially private embedding models are both framework- and dataindependent, which facilitates the deployment and sharing. The source code is available at https://github.com/sonvx/dpText.

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