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Clough, P.; Tang, J.; Hall, M.H.; Warner, A.: Linking archival data to location : a case study at the UK National Archives (2011)
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- Abstract
- Purpose - The National Archives (TNA) is the UK Government's official archive. It stores and maintains records spanning over a 1,000 years in both physical and digital form. Much of the information held by TNA includes references to place and frequently user queries to TNA's online catalogue involve searches for location. The purpose of this paper is to illustrate how TNA have extracted the geographic references in their historic data to improve access to the archives. Design/methodology/approach - To be able to quickly enhance the existing archival data with geographic information, existing technologies from Natural Language Processing (NLP) and Geographical Information Retrieval (GIR) have been utilised and adapted to historical archives. Findings - Enhancing the archival records with geographic information has enabled TNA to quickly develop a number of case studies highlighting how geographic information can improve access to large-scale archival collections. The use of existing methods from the GIR domain and technologies, such as OpenLayers, enabled one to quickly implement this process in a way that is easily transferable to other institutions. Practical implications - The methods and technologies described in this paper can be adapted, by other archives, to similarly enhance access to their historic data. Also the data-sharing methods described can be used to enable the integration of knowledge held at different archival institutions. Originality/value - Place is one of the core dimensions for TNA's archival data. Many of the records which are held make reference to place data (wills, legislation, court cases), and approximately one fifth of users' searches involve place names. However, there are still a number of open questions regarding the adaptation of existing GIR methods to the history domain. This paper presents an overview over available GIR methods and the challenges in applying them to historical data.
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Li, D.; Luo, Z.; Ding, Y.; Tang, J.; Sun, G.G.-Z.; Dai, X.; Du, J.; Zhang, J.; Kong, S.: User-level microblogging recommendation incorporating social influence (2017)
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- Abstract
- With the information overload of user-generated content in microblogging, users find it extremely challenging to browse and find valuable information in their first attempt. In this paper we propose a microblogging recommendation algorithm, TSI-MR (Topic-Level Social Influence-based Microblogging Recommendation), which can significantly improve users' microblogging experiences. The main innovation of this proposed algorithm is that we consider social influences and their indirect structural relationships, which are largely based on social status theory, from the topic level. The primary advantage of this approach is that it can build an accurate description of latent relationships between two users with weak connections, which can improve the performance of the model; furthermore, it can solve sparsity problems of training data to a certain extent. The realization of the model is mainly based on Factor Graph. We also applied a distributed strategy to further improve the efficiency of the model. Finally, we use data from Tencent Weibo, one of the most popular microblogging services in China, to evaluate our methods. The results show that incorporating social influence can improve microblogging performance considerably, and outperform the baseline methods.
- Source
- Journal of the Association for Information Science and Technology. 68(2017) no.3, S.553-568
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Li, D.; Tang, J.; Ding, Y.; Shuai, X.; Chambers, T.; Sun, G.; Luo, Z.; Zhang, J.: Topic-level opinion influence model (TOIM) : an investigation using tencent microblogging (2015)
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- Abstract
- Text mining has been widely used in multiple types of user-generated data to infer user opinion, but its application to microblogging is difficult because text messages are short and noisy, providing limited information about user opinion. Given that microblogging users communicate with each other to form a social network, we hypothesize that user opinion is influenced by its neighbors in the network. In this paper, we infer user opinion on a topic by combining two factors: the user's historical opinion about relevant topics and opinion influence from his/her neighbors. We thus build a topic-level opinion influence model (TOIM) by integrating both topic factor and opinion influence factor into a unified probabilistic model. We evaluate our model in one of the largest microblogging sites in China, Tencent Weibo, and the experiments show that TOIM outperforms baseline methods in opinion inference accuracy. Moreover, incorporating indirect influence further improves inference recall and f1-measure. Finally, we demonstrate some useful applications of TOIM in analyzing users' behaviors in Tencent Weibo.
- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2657-2673
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Huang, H.; Andrews, J.; Tang, J.: Citation characterization and impact normalization in bioinformatics journals (2012)
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- Source
- Journal of the American Society for Information Science and Technology. 63(2012) no.3, S.490-497
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Li, D.; Ding, Y.; Sugimoto, C.; He, B.; Tang, J.; Yan, E.; Lin, N.; Qin, Z.; Dong, T.: Modeling topic and community structure in social tagging : the TTR-LDA-Community model (2011)
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- Source
- Journal of the American Society for Information Science and Technology. 62(2011) no.9, S.1849-1866
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Lin, N.; Li, D.; Ding, Y.; He, B.; Qin, Z.; Tang, J.; Li, J.; Dong, T.: ¬The dynamic features of Delicious, Flickr, and YouTube (2012)
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- Source
- Journal of the American Society for Information Science and Technology. 63(2012) no.1, S.139-162
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Ru, C.; Tang, J.; Li, S.; Xie, S.; Wang, T.: Using semantic similarity to reduce wrong labels in distant supervision for relation extraction (2018)
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- Source
- Information processing and management. 54(2018) no.4, S.593-608
