Cane Wing-ki Leung

Wisers (China)

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Stephen Chan

Hong Kong Polytechnic University

Fu-Lai Chung

Hong Kong Polytechnic University

Yangqiu Song

Tsinghua University

Ee‐Peng Lim

Singapore Management University

Guanqing Liang

Wisers (China)

Qiang Yang

Hong Kong University of Science and Technology

Juanzi Li

Tsinghua University

Pengfei Liu

Xinjiang Medical University

Hongming Zhang

Bellevue Hospital Center

Chao He

Affiliated Hospital of Shaanxi University of Chinese Medicine

Cooperative Institutions

Hong Kong Polytechnic University

University of Hong Kong

Tsinghua University

Hong Kong University of Science and Technology

Singapore Management University

Wisers (China)

Chinese University of Hong Kong

Zhejiang University

University of Electronic Science and Technology of China

City University of Hong Kong

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ASER: A Large-scale Eventuality Knowledge Graph

Hongming Zhang Xin Liu Haojie Pan Yangqiu Song Cane Wing-ki Leung

Understanding human’s language requires complex world knowledge. However, existing large-scale knowledge graphs mainly focus on knowledge about entities while ignoring knowledge about activities, states, or events, which are used to describe how entities or things act in the real world. To fill this gap, we develop ASER (activities, states, events, and their relations), a large-scale eventuality knowledge graph extracted from more than 11-billion-token unstructured textual data. ASER contains 15 relation types belonging to five categories, 194-million unique eventualities, and 64-million unique edges among them. Both intrinsic and extrinsic evaluations demonstrate the quality and effectiveness of ASER.

Knowledge graph

10.1145/3366423.3380107

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Citations (115)

An empirical study of a cross-level association rule mining approach to cold-start recommendations

Knowledge-Based Systems (2008)

Cane Wing-ki Leung Stephen Chan Fu-Lai Chung

Cold start (automotive)

Association (psychology)

Empirical Research

10.1016/j.knosys.2008.03.012

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Citations (94)

Improving Model Generalization: A Chinese Named Entity Recognition Case Study

Guanqing Liang Cane Wing-ki Leung

Guanqing Liang, Cane Wing-Ki Leung. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 2021.

Computational linguistics

Named Entity Recognition

Association (psychology)

10.18653/v1/2021.acl-short.125

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Citations (3)

Exploiting Effective Representations for Chinese Sentiment Analysis Using a Multi-Channel Convolutional Neural Network

arXiv (Cornell University) (2018)

Pengfei Liu Ji Zhang Cane Wing-ki Leung Chao He Thomas L. Griffiths

Effective representation of a text is critical for various natural language processing tasks. For the particular task of Chinese sentiment analysis, it is important to understand and choose an effective representation of a text from different forms of Chinese representations such as word, character and pinyin. This paper presents a systematic study of the effect of these representations for Chinese sentiment analysis by proposing a multi-channel convolutional neural network (MCCNN), where each channel corresponds to a representation. Experimental results show that: (1) Word wins on the dataset of low OOV rate while character wins otherwise; (2) Using these representations in combination generally improves the performance; (3) The representations based on MCCNN outperform conventional ngram features using SVM; (4) The proposed MCCNN model achieves the competitive performance against the state-of-the-art model fastText for Chinese sentiment analysis.

Pinyin

Sentiment Analysis

Representation

10.48550/arxiv.1808.02961

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Citations (3)

What Do People Want in Microblogs? Measuring Interestingness of Hashtags in Twitter

Jianshu Weng Ee‐Peng Lim Qi He Cane Wing-ki Leung

When micro logging becomes a very popular social media, finding interesting posts from high volume stream of user posts is a challenging research problem. To organize large number of posts, users can assign tags to posts so that these posts can be navigated and searched by tag. In this paper, we focus on modeling the interestingness of hash tags in Twitter, the largest and most active micro logging site. We propose to first construct communities based on both follow links and tagged interactions. We then measure the dispersion and divergence of users and tweets using hash tags among the constructed communities. The interestingness of hash tags are then derived from these community-based dispersion and divergence features. We further introduce a supervised approach to rank hash tags by interestingness. Our experiments on a Twitter dataset show that the proposed approach achieves a fairly good performance.

Microblogging

Divergence (linguistics)

Rank (graph theory)

Leverage (statistics)

10.1109/icdm.2010.34

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Citations (20)

Mining interesting link formation rules in social networks

Cane Wing-ki Leung Ee‐Peng Lim David Lo Jianshu Weng

Link structures are important patterns one looks out for when modeling and analyzing social networks. In this paper, we propose the task of mining interesting Link Formation rules (LF-rules) containing link structures known as Link Formation patterns (LF-patterns). LF-patterns capture various dyadic and/or triadic structures among groups of nodes, while LF-rules capture the formation of a new link from a focal node to another node as a postcondition of existing connections between the two nodes. We devise a novel LF-rule mining algorithm, known as LFR-Miner, based on frequent subgraph mining for our task. In addition to using a support-confidence framework for measuring the frequency and significance of LF-rules, we introduce the notion of expected support to account for the extent to which LF-rules exist in a social network by chance. Specifically, only LF-rules with higher-than-expected support are considered interesting. We conduct empirical studies on two real-world social networks, namely Epinions and myGamma. We report interesting LF-rules mined from the two networks, and compare our findings with earlier findings in social network analysis.

Link (geometry)

Link analysis

10.1145/1871437.1871468

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Citations (44)

Supporting real‐time collaborative learning with web‐based groupware

Innovations in Education and Teaching International (2005)

Stephen Chan Cane Wing-ki Leung Cassidy Y. Yeung Chi-Yin Chow Edward W. C. Tsui

The Wireless Online Tutoring System (WOTS) is a web‐based, synchronous groupware system that has been developed to facilitate student collaboration in group projects. It supports interactive and collaborative structured‐diagramming and is equipped with group awareness features such as remote cursors, locking, and text‐based instant messaging. Users can access WOTS from both personal computers and handheld devices, allowing it to be used in wireless classrooms or distributed environments. Tests on student software design projects validate the ability of WOTS to support student collaboration in group projects, and show that its architecture is scalable and reliable. Its performance is expected to improve as more powerful handheld devices and wireless technologies come on to the market.

Collaborative software

10.1080/14703290500268469

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Citations (2)

Instilling Social to Physical: Co-Regularized Heterogeneous Transfer Learning

Proceedings of the AAAI Conference on Artificial Intelligence (2016)

Ying Wei Yin Zhu Cane Wing-ki Leung Yangqiu Song Qiang Yang

Ubiquitous computing tasks, such as human activity recognition (HAR), are enabling a wide spectrum of applications, ranging from healthcare to environment monitoring. The success of a ubiquitous computing task relies on sufﬁcient physical sensor data with groundtruth labels, which are always scarce due to the expensive annotating process. Meanwhile, social media platforms provide a lot of social or semantic context information. People share what they are doing and where they are frequently in the messages they post. This rich set of socially shared activities motivates us to transfer knowledge from social media to address the sparsity issue of labelled physical sensor data. In order to transfer the knowledge of social and semantic context, we propose a Co-Regularized Heterogeneous Transfer Learning (CoHTL) model, which builds a common semantic space derived from two heterogeneous domains. Our proposed method outperforms state-of-the-art methods on two ubiquitous computing tasks, namely human activity recognition and region function discovery.

Transfer of learning

Knowledge Transfer

10.1609/aaai.v30i1.10172

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Citations (47)

Role-aware conformity influence modeling and analysis in social networks

National Conference on Artificial Intelligence (2014)

Jing Zhang Jie Tang Honglei Zhuang Cane Wing-ki Leung Juanzi Li

Conformity influence is the inclination of a person to be influenced by others. In this paper, we study how the conformity tendency of a person changes with her role, as defined by her structural properties in a social network. We first formalize conformity influence using a utility function based on the conformity theory from social psychology, and then propose a probabilistic graphical model, referred to as Role-Conformity Model (RCM), for modeling the role-aware conformity influence between users by incorporating the utility function. We apply the proposed RCM to several academic research networks, and discover that people with higher degree and lower clustering coefficient are more likely to conform to others.We also evaluate RCM through the task of word usage prediction in academic publications, and show significant improvements over baseline models.

Conformity

Clustering coefficient

Social network (sociolinguistics)

Source

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Citations (15)

Applying Cross-Level Association Rule Mining to Cold-Start Recommendations

2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops (2007)

Cane Wing-ki Leung Stephe Chan Fu-Lai Chung

We propose a novel hybrid recommendation algorithm for addressing the well-known cold-start problem in Collaborative Filtering (CF). Our algorithm makes use of Cross- Level Association RulEs (CLARE) to integrate content information about domain items into collaborative filters. We first introduce a preference model comprising both user- item and item-item relationships in recommender systems, and then describe how the CLARE algorithm generates recommendations for cold-start items based on the preference model. Experimental results validated that CLARE is capable of recommending cold-start items, and that it increases the number of recommendable items significantly by addressing the cold-start problem.

Cold start (automotive)

Association (psychology)

10.1109/wi-iatw.2007.22

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Citations (13)