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184 results on '"Wan, Changxuan"'

1. Token-Event-Role Structure-based Multi-Channel Document-Level Event Extraction

Author

Wan, Qizhi, Wan, Changxuan, Xiao, Keli, Xiong, Hui, Liu, Dexi, and Liu, Xiping

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Document-level event extraction is a long-standing challenging information retrieval problem involving a sequence of sub-tasks: entity extraction, event type judgment, and event type-specific multi-event extraction. However, addressing the problem as multiple learning tasks leads to increased model complexity. Also, existing methods insufficiently utilize the correlation of entities crossing different events, resulting in limited event extraction performance. This paper introduces a novel framework for document-level event extraction, incorporating a new data structure called token-event-role and a multi-channel argument role prediction module. The proposed data structure enables our model to uncover the primary role of tokens in multiple events, facilitating a more comprehensive understanding of event relationships. By leveraging the multi-channel prediction module, we transform entity and multi-event extraction into a single task of predicting token-event pairs, thereby reducing the overall parameter size and enhancing model efficiency. The results demonstrate that our approach outperforms the state-of-the-art method by 9.5 percentage points in terms of the F1 score, highlighting its superior performance in event extraction. Furthermore, an ablation study confirms the significant value of the proposed data structure in improving event extraction tasks, further validating its importance in enhancing the overall performance of the framework.

Published
2023

2. An Emotion Aware Dual-Context Model for Suicide Risk Assessment on Social Media

Author

Liang, Zifang, Liu, Dexi, Wan, Qizhi, Liu, Xiping, Liao, Guoqiong, Wan, Changxuan, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Wu, Feng, editor, Huang, Xuanjing, editor, He, Xiangnan, editor, Tang, Jiliang, editor, Zhao, Shu, editor, Li, Daifeng, editor, and Zhang, Jing, editor

Published
2024
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5. An Intelligent Group Event Recommendation System in Social networks

Author

Liao, Guoqiong, Huang, Xiaomei, Xiong, Neal N., and Wan, Changxuan

Subjects
Computer Science - Social and Information Networks, Computer Science - Information Retrieval
Abstract

The importance of contexts has been widely recognized in recommender systems for individuals. However, most existing group recommendation models in Event-Based Social Networks (EBSNs) focus on how to aggregate group members' preferences to form group preferences. In these models, the influence of contexts on groups is considered but simply defined in a manual way, which cannot model the complex and deep interactions between contexts and groups. In this paper, we propose an Attention-based Context-aware Group Event Recommendation model (ACGER) in EBSNs. ACGER models the deep, non-linear influence of contexts on users, groups, and events through multi-layer neural networks. Especially, a novel attention mechanism is designed to enable the influence weights of contexts on users/groups change dynamically with the events concerned. Considering that groups may have completely different behavior patterns from group members, we propose that the preference of a group need to be obtained from indirect and direct perspectives (called indirect preference and direct preference respectively). In order to obtain the indirect preference, we propose a method of aggregating preferences based on attention mechanism. Compared with existing predefined strategies, this method can flexibly adapt the strategy according to the events concerned by the group. In order to obtain the direct preference, we employ neural networks to directly learn it from group-event interactions. Furthermore, to make full use of rich user-event interactions in EBSNs, we integrate the context-aware individual recommendation task into ACGER, which enhances the accuracy of learning of user embeddings and event embeddings. Extensive experiments on two real datasets from Meetup show that our model ACGER significantly outperforms the state-of-the-art models.

Published
2020

6. Attention-aware Next Event Recommendation Strategy for Groups

Author

LIAO Guoqiong, YANG Lechuan, WAN Changxuan, LIU Dexi, LIU Xiping

Subjects
event-based social networks (ebsn), next event recommendation, group recommendation, attention mechanism, multi-label classification, Electronic computers. Computer science, QA75.5-76.95
Abstract

In recent years, event-based social networks (EBSN) have gradually become an effective way for people to choose social events, and how to accurately recommend events to users or groups in need has become an important topic in this field. Next item recommendation can capture users’ dynamic preferences and has been well developed in e-commerce and other fields. However, there are less researches on next event recommendation for groups in EBSN. This paper mainly studies the group-oriented next event recommendation strategy, but due to the dynamic change of group preference, short event life cycle and cold start of new events, it is more difficult to recommend the next event for groups. Firstly, based on the characteristic that group preferences change dynamically over time, the history interaction records of group are divided into each period. Considering the sparsity of the member data due to the period division, which is unfavorable to group preference modeling, an engagement-based ranking strategy is proposed to extract the preferences of core members in the current period, and the attention mechanism is used to fuse them to the group static preferences. Then, the group dynamic preference is obtained by combining the static preference of each period with the attention-based sequential model. Finally, the multi-label classification problem is introduced into the event recommendation, which regards the contexts as labels of the event, and makes the model predict the probability distribution of each context to match the event, so as to alleviate the new event cold start problem. Experimental results verify that the proposed strategy has good performance.

Published
2023
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9. FHAN: Feature-Level Hierarchical Attention Network for Group Event Recommendation

Author

Liao, Guoqiong, Deng, Xiaobin, Huang, Xiaomei, Wan, Changxuan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Xin, editor, Zhang, Rui, editor, Lee, Young-Koo, editor, Sun, Le, editor, and Moon, Yang-Sae, editor

Published
2020
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11. Token-Event-Role Structure-Based Multi-Channel Document-Level Event Extraction

Author

Wan, Qizhi, Wan, Changxuan, Xiao, Keli, Xiong, Hui, Liu, Dexi, Liu, Xiping, Hu, Rong, Wan, Qizhi, Wan, Changxuan, Xiao, Keli, Xiong, Hui, Liu, Dexi, Liu, Xiping, and Hu, Rong

Abstract

Document-level event extraction is a long-standing challenging information retrieval problem involving a sequence of sub-tasks: entity extraction, event type judgment, and event type-specific multi-event extraction. However, addressing the problem as multiple learning tasks leads to increased model complexity. Also, existing methods insufficiently utilize the correlation of entities crossing different events, resulting in limited event extraction performance. This article introduces a novel framework for document-level event extraction, incorporating a newdata structure called token-event-role and a multi-channel argument role predictionmodule. The proposed data structure enables our model to uncover the primary role of tokens in multiple events, facilitating a more comprehensive understanding of event relationships. By leveraging the multi-channel prediction module, we transform entity and multi-event extraction into a single task of predicting token-event pairs, thereby reducing the overall parameter size and enhancing model efficiency. The results demonstrate that our approach outperforms the state-of-the-art method by 9.5 percentage points in terms of the F1 score, highlighting its superior performance in event extraction. Furthermore, an ablation study confirms the significant value of the proposed data structure in improving event extraction tasks, further validating its importance in enhancing the overall performance of the framework

Published
2024

12. What Are You Reading: A Big Data Analysis of Online Literary Content

Author

Liu, Xiping, Wan, Changxuan, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Tan, Ying, editor, and Shi, Yuhui, editor

Published
2019
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16. rHDP: An Aspect Sharing-Enhanced Hierarchical Topic Model for Multi-Domain Corpus.

Author

Zhang, Yitao, Wan, Changxuan, Xiao, Keli, Wan, Qizhi, Liu, Dexi, and Liu, Xiping

Abstract

The article focuses on rHDP, a hierarchical topic model for multi-domain corpora, addressing challenges in identifying inter-topic correlations and aspect-sharing patterns. Topics include the enhancement of nested Chinese restaurant processes for aspect-sharing pattern extraction, the development of a hierarchical Gibbs sampling scheme for model inference, and the validation of the model's efficacy through experiments on public and financial document datasets.

Published
2024
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20. Two-Phase Mining for Frequent Closed Episodes

Author

Liao, Guoqiong, Yang, Xiaoting, Xie, Sihong, Yu, Philip S., Wan, Changxuan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Cui, Bin, editor, Zhang, Nan, editor, Xu, Jianliang, editor, Lian, Xiang, editor, and Liu, Dexi, editor

Published
2016
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25. Cluster Labeling Extraction and Ranking Feature Selection for High Quality XML Pseudo Relevance Feedback Fragments Set

Author

Zhong, Minjuan, Wan, Changxuan, Liu, Dexi, Liao, Shumei, Luo, Siwen, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Motoda, Hiroshi, editor, Wu, Zhaohui, editor, Cao, Longbing, editor, Zaiane, Osmar, editor, Yao, Min, editor, and Wang, Wei, editor

Published
2013
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26. JUFE at INEX 2011 Snippet Retrieval Track

Author

Liu, Dexi, Wan, Changxuan, Liao, Guoqiong, Zhong, Minjuan, Liu, Xiping, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Geva, Shlomo, editor, Kamps, Jaap, editor, and Schenkel, Ralf, editor

Published
2012
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27. A Practice Probability Frequent Pattern Mining Method over Transactional Uncertain Data Streams

Author

Liao, Guoqiong, Wu, Linqing, Wan, Changxuan, Xiong, Naixue, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Hsu, Ching-Hsien, editor, Yang, Laurence T., editor, Ma, Jianhua, editor, and Zhu, Chunsheng, editor

Published
2011
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33. Efficient Evaluation of Sibling Relationship in XPath Queries

Author

Wan, Changxuan, Liu, Xiping, Lin, Dahai, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Grumbach, Stéphane, editor, Sui, Liying, editor, and Vianu, Victor, editor

Published
2005
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47. Approximately Filtering Redundant Data for Uncertain RFID Data Streams

Author

Liao Guoqiong, Liu Xiping, Huang Zhiwei, Huang Xiaomei, Hui Ni, Zhou Jun, and Wan Changxuan

Subjects
Data stream, business.industry, Data stream mining, Computer science, Real-time computing, Probabilistic logic, 020207 software engineering, 02 engineering and technology, Bloom filter, Filter (signal processing), computer.software_genre, 020204 information systems, Sliding window protocol, 0202 electrical engineering, electronic engineering, information engineering, Radio-frequency identification, Data mining, business, computer, Block (data storage)
Abstract

Nowadays, Radio Frequency Identification (RFID) technology has been widely employed in the fields of object positioning, tracking and monitoring. However, there are a large number of redundant data generated in RFID systems due to duplicate detection and cross detection. Since RFID data is usually streaming, uncertain and mobile data, traditional static data and data stream filtering strategies cannot be applied to filter the RFID data effectively. In the paper, we first present a three-phase filtering framework under a block-based sliding window model. Aiming to filter the temporal redundant events, we propose an approximate Probability Synthesis Bloom Filter (PSBF) and discuss its filter principle, update rules and error rate in details. Comparing with the existing RFID filters, PSBF can not only filter the redundant probabilistic events, but also can calculate object existential probabilities with temporal decaying, and handle with the situations of location movement and staying at the overlapping areas among multiple readers correctly. The experiments on the simulated dataset show that the proposed filter outperforms the state-of-the-art filtering method.

Published
2017
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49. Chinese Morpheme-based Emotion Detection on Financial Securities Information

Author

Bian Hairong, Wan Changxuan, Yang Li, and Li Guolin

Subjects
Value (ethics), Finance, Scheme (programming language), Computer Networks and Communications, business.industry, Computer science, Emotion detection, Emotional intensity, Symbol (chemistry), Field (computer science), Domain (software engineering), Hardware and Architecture, Morpheme, business, computer, computer.programming_language
Abstract

With the unceasing improvement of data mining and natural language processing technology, more and more researchers devote themselves to comment resources on the Web. While there are no readymade emotional corpora in the financial and securities domain, emotional analysis applied in this field is still rare. As financial information is usually in the form of unstructured Web Text, this paper fully considers characteristics of financial information, and analyzes the emotion of Web Text by calculating their emotional inclination values based on evaluation scores of morpheme. For each document we compute an emotional value. Its symbol indicates the emotional inclination, and its absolute value reflects the emotional intensity. Thus, this can prevent the limitation of lacking emotional corpus in the financial and securities domain. Experimental results demonstrate that this presented scheme optimizes the existing main schemes to effectively analyze the emotional inclinations of Web financial information.

Published
2012
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