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Using Content-Level Structures For Summarizing Microblog Repost Trees, Jing Li, Wei Gao, Zhongyu Wei, Baolin Peng, Kam-Fai Wong
Using Content-Level Structures For Summarizing Microblog Repost Trees, Jing Li, Wei Gao, Zhongyu Wei, Baolin Peng, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
A microblog repost tree provides strong clues on how an event described therein develops. To help social media users capture the main clues of events on microblogging sites, we propose a novel repost tree summarization framework by effectively differentiating two kinds of messages on repost trees called leaders and followers, which are derived from contentlevel structure information, i.e., contents of messages and the reposting relations. To this end, Conditional Random Fields (CRF) model is used to detect leaders across repost tree paths. We then present a variant of random-walk-based summarization model to rank and select salient messages based on the …
Faitcrowd: Fine Grained Truth Discovery For Crowdsourced Data Aggregation, Fenglong Ma, Yaliang Li, Qi Li, Minghui Qiu, Jing Gao, Shi Zhi, Lu Su, Bo Zhao, Jiawei Han
Faitcrowd: Fine Grained Truth Discovery For Crowdsourced Data Aggregation, Fenglong Ma, Yaliang Li, Qi Li, Minghui Qiu, Jing Gao, Shi Zhi, Lu Su, Bo Zhao, Jiawei Han
Research Collection School Of Computing and Information Systems
In crowdsourced data aggregation task, there exist conflicts in the answers provided by large numbers of sources on the same set of questions. The most important challenge for this task is to estimate source reliability and select answers that are provided by high-quality sources. Existing work solves this problem by simultaneously estimating sources' reliability and inferring questions' true answers (i.e., the truths). However, these methods assume that a source has the same reliability degree on all the questions, but ignore the fact that sources' reliability may vary significantly among different topics. To capture various expertise levels on different topics, we …
Tweet Sentiment: From Classification To Quantification, Wei Gao, Fabrizio Sebastiani
Tweet Sentiment: From Classification To Quantification, Wei Gao, Fabrizio Sebastiani
Research Collection School Of Computing and Information Systems
Sentiment classification has become a ubiquitous enabling technology in the Twittersphere, since classifying tweets according to the sentiment they convey towards a given entity (be it a product, a person, a political party, or a policy) has many applications in political science, social science, market research, and many others. In this paper we contend that most previous studies dealing with tweet sentiment classification (TSC) use a suboptimal approach. The reason is that the final goal of most such studies is not estimating the class label (e.g., Positive, Negative, or Neutral) of individual tweets, but estimating the relative frequency (a.k.a. "prevalence") …
Gibberish, Assistant, Or Master? Using Tweets Linking To News For Extractive Single-Document Summarization, Zhongyu Wei, Wei Gao
Gibberish, Assistant, Or Master? Using Tweets Linking To News For Extractive Single-Document Summarization, Zhongyu Wei, Wei Gao
Research Collection School Of Computing and Information Systems
Single-document summarization is a challenging task. In this paper, we explore effective ways using the tweets linking to news for generating extractive summary of each document. We reveal the very basic value of tweets that can be utilized by regarding every tweet as a vote for candidate sentences. Base on such finding, we resort to unsupervised summarization models by leveraging the linking tweets to master the ranking of candidate extracts via random walk on a heterogeneous graph. The advantage is that we can use the linking tweets to opportunistically "supervise" the summarization with no need of reference summaries. Furthermore, we …
Topic Modeling With Document Relative Similarities, Jianguang Du, Jing Jiang, Dandan Song, Lejian Liao
Topic Modeling With Document Relative Similarities, Jianguang Du, Jing Jiang, Dandan Song, Lejian Liao
Research Collection School Of Computing and Information Systems
Topic modeling has been widely used in text mining. Previous topic models such as Latent Dirichlet Allocation (LDA) are successful in learning hidden topics but they do not take into account metadata of documents. To tackle this problem, many augmented topic models have been proposed to jointly model text and metadata. But most existing models handle only categorical and numerical types of metadata. We identify another type of metadata that can be more natural to obtain in some scenarios. These are relative similarities among documents. In this paper, we propose a general model that links LDA with constraints derived from …
Using Tweets To Help Sentence Compression For News Highlights Generation, Zhongyu Wei, Yang Liu, Chen Li, Wei Gao
Using Tweets To Help Sentence Compression For News Highlights Generation, Zhongyu Wei, Yang Liu, Chen Li, Wei Gao
Research Collection School Of Computing and Information Systems
We explore using relevant tweets of a given news article to help sentence compression for generating compressive news highlights. We extend an unsupervised dependency-tree based sentence compression approach by incorporating tweet information to weight the tree edge in terms of informativeness and syntactic importance. The experimental results on a public corpus that contains both news articles and relevant tweets show that our proposed tweets guided sentence compression method can improve the summarization performance significantly compared to the baseline generic sentence compression method.
Solar: Scalable Online Learning Algorithms For Ranking, Jialei Wang, Ji Wan, Yongdong Zhang, Steven C. H. Hoi
Solar: Scalable Online Learning Algorithms For Ranking, Jialei Wang, Ji Wan, Yongdong Zhang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Traditional learning to rank methods learn ranking models from training data in a batch and offline learning mode, which suffers from some critical limitations, e.g., poor scalability as the model has to be retrained from scratch whenever new training data arrives. This is clearly nonscalable for many real applications in practice where training data often arrives sequentially and frequently. To overcome the limitations, this paper presents SOLAR- a new framework of Scalable Online Learning Algorithms for Ranking, to tackle the challenge of scalable learning to rank. Specifically, we propose two novel SOLAR algorithms and analyze their IR measure bounds theoretically. …
A Convolution Kernel Approach To Identifying Comparisons In Text, Maksim Tkachenko, Hady W. Lauw
A Convolution Kernel Approach To Identifying Comparisons In Text, Maksim Tkachenko, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Comparisons in text, such as in online reviews, serve as useful decision aids. In this paper, we focus on the task of identifying whether a comparison exists between a specific pair of entity mentions in a sentence. This formulation is transformative, as previous work only seeks to determine whether a sentence is comparative, which is presumptuous in the event the sentence mentions multiple entities and is comparing only some, not all, of them. Our approach leverages not only lexical features such as salient words, but also structural features expressing the relationships among words and entity mentions. To model these features …
A Hassle-Free Unsupervised Domain Adaptation Method Using Instance Similarity Features, Jianfei Yu, Jing Jiang
A Hassle-Free Unsupervised Domain Adaptation Method Using Instance Similarity Features, Jianfei Yu, Jing Jiang
Research Collection School Of Computing and Information Systems
We present a simple yet effective unsupervised domain adaptation method that can be generally applied for different NLP tasks. Our method uses unlabeled target domain instances to induce a set of instance similarity features. These features are then combined with the original features to represent labeled source domain instances. Using three NLP tasks, we show that our method consistently out-performs a few baselines, including SCL, an existing general unsupervised domain adaptation method widely used in NLP. More importantly, our method is very easy to implement and incurs much less computational cost than SCL.
Qcri: Answer Selection For Community Question Answering - Experiment For Arabic And English, Massimo Nicosia, Simone Filice, Alberto Barron-Cedeno, Iman Saleh, Hamdy Mubarak, Wei Gao, Preslav Nakov, Giovanni Da San Martino, Alessandro Moschitti, Kareem Darwish, Lluis Marquz Marquz, Shafiq Joty, Walid Magdy Magdy
Qcri: Answer Selection For Community Question Answering - Experiment For Arabic And English, Massimo Nicosia, Simone Filice, Alberto Barron-Cedeno, Iman Saleh, Hamdy Mubarak, Wei Gao, Preslav Nakov, Giovanni Da San Martino, Alessandro Moschitti, Kareem Darwish, Lluis Marquz Marquz, Shafiq Joty, Walid Magdy Magdy
Research Collection School Of Computing and Information Systems
This paper describes QCRI’s participation in SemEval-2015 Task 3 “Answer Selection in Community Question Answering”, which targeted real-life Web forums, and was offered in both Arabic and English. We apply a supervised machine learning approach considering a manifold of features including among others word n-grams, text similarity, sentiment analysis, the presence of specific words, and the context of a comment. Our approach was the best performing one in the Arabic subtask and the third best in the two English subtasks
Active Semi-Supervised Defect Categorization, Ferdian Thung, Xuan-Bach D. Le, David Lo
Active Semi-Supervised Defect Categorization, Ferdian Thung, Xuan-Bach D. Le, David Lo
Research Collection School Of Computing and Information Systems
Defects are inseparable part of software development and evolution. To better comprehend problems affecting a software system, developers often store historical defects and these defects can be categorized into families. IBM proposes Orthogonal Defect Categorization (ODC) which include various classifications of defects based on a number of orthogonal dimensions (e.g., symptoms and semantics of defects, root causes of defects, etc.). To help developers categorize defects, several approaches that employ machine learning have been proposed in the literature. Unfortunately, these approaches often require developers to manually label a large number of defect examples. In practice, manually labelling a large number of …
Tasknav: Task-Based Navigation Of Software Documentation, Christoph Treude, Mathieu Sicard, Marc Klocke, Martin P. Robillard
Tasknav: Task-Based Navigation Of Software Documentation, Christoph Treude, Mathieu Sicard, Marc Klocke, Martin P. Robillard
Research Collection School Of Computing and Information Systems
To help developers navigate documentation, we introduce Task Nav, a tool that automatically discovers and indexes task descriptions in software documentation. With Task Nav, we conceptualize tasks as specific programming actions that have been described in the documentation. Task Nav presents these extracted task descriptions along with concepts, code elements, and section headers in an auto-complete search interface. Our preliminary evaluation indicates that search results identified through extracted task descriptions are more helpful to developers than those found through other means, and that they help bridge the gap between documentation structure and the information needs of software developers. Video: https://www.youtube.com/watch?v=opnGYmMGnqY.
Flutcha: Using Fluency To Distinguish Humans From Computers, Kotaro Hara, Mohammad Taghi Hajiaghayi, Benjamin B. Benderson
Flutcha: Using Fluency To Distinguish Humans From Computers, Kotaro Hara, Mohammad Taghi Hajiaghayi, Benjamin B. Benderson
Research Collection School Of Computing and Information Systems
Improvements in image understanding technologies aremaking it possible for computers to pass traditionalCAPTCHA tests with high probability. This suggests theneed for new kinds of tasks that are easy to accomplishfor humans but remain difficult for computers. In thispaper, we introduce Fluency CAPTCHA (FluTCHA), anovel method to distinguish humans from computersusing the fact that humans are better than machines atimproving the fluency of sentences. We propose a wayto let users work on FluTCHA tests and simultaneouslycomplete useful linguistic tasks. Evaluation studiesdemonstrate the feasibility of using FluTCHA todistinguish humans from computers.
Rclinker: Automated Linking Of Issue Reports And Commits Leveraging Rich Contextual Information, Tien-Duy B. Le, Mario Linares Vasquez, David Lo, Denys Poshyvanyk
Rclinker: Automated Linking Of Issue Reports And Commits Leveraging Rich Contextual Information, Tien-Duy B. Le, Mario Linares Vasquez, David Lo, Denys Poshyvanyk
Research Collection School Of Computing and Information Systems
Links between issue reports and their corresponding commits in version control systems are often missing. However, these links are important for measuring the quality of a software system, predicting defects, and many other tasks. Several approaches have been designed to solve this problem by automatically linking bug reports to source code commits via comparison of textual information in commit messages and bug reports. Yet, the effectiveness of these techniques is oftentimes suboptimal when commit messages are empty or contain minimum information; this particular problem makes the process of recovering traceability links between commits and bug reports particularly challenging. In this …
Characterizing Silent Users In Social Media Communities, Wei Gong, Ee-Peng Lim, Feida Zhu
Characterizing Silent Users In Social Media Communities, Wei Gong, Ee-Peng Lim, Feida Zhu
Research Collection School Of Computing and Information Systems
Silent users often constitute a significant proportion of an online user-generated content system. In the context of social media such as Twitter, users can opt to be silent all or most of the time. They are often called the invisible participants or lurkers. As lurkers contribute little to the online content, existing analysis often overlooks their presence and voices. However, we argue that understanding lurkers is important in many applications such as recommender systems, targeted advertising, and social sensing. This research therefore seeks to characterize lurkers in social media and propose methods to profile them. We examine 18 weeks of …
Advances In Knowledge Discovery And Data Mining Part Ii, Tru Cao, Ee Peng Lim, Zhi-Hua Zhou, Tu-Bao Ho, David Wai-Lok Cheung, Hiroshi Motoda
Advances In Knowledge Discovery And Data Mining Part Ii, Tru Cao, Ee Peng Lim, Zhi-Hua Zhou, Tu-Bao Ho, David Wai-Lok Cheung, Hiroshi Motoda
Research Collection School Of Computing and Information Systems
No abstract provided.
Exploring Cyberbullying And Other Toxic Behavior In Team Competition Online Games, Haewoon Kwak, Jeremy Blackburn, Seungyeop. Han
Exploring Cyberbullying And Other Toxic Behavior In Team Competition Online Games, Haewoon Kwak, Jeremy Blackburn, Seungyeop. Han
Research Collection School Of Computing and Information Systems
In this work we explore cyberbullying and other toxic behavior in team competition online games. Using a dataset of over 10 million player reports on 1.46 million toxic players along with corresponding crowdsourced decisions, we test several hypotheses drawn from theories explaining toxic behavior. Besides providing large-scale, empirical based understanding of toxic behavior, our work can be used as a basis for building systems to detect, prevent, and counter-act toxic behavior.
Multi-Roles Affiliation Model For General User Profiling, Lizi Liao, Heyan Huang, Yashen Wang
Multi-Roles Affiliation Model For General User Profiling, Lizi Liao, Heyan Huang, Yashen Wang
Research Collection School Of Computing and Information Systems
Online social networks release user attributes, which is important for many applications. Due to the sparsity of such user attributes online, many works focus on profiling user attributes automatically. However, in order to profile a specific user attribute, an unique model is built and such model usually does not fit other profiling tasks. In our work, we design a novel, flexible general user profiling model which naturally models users’ friendships with user attributes. Experiments show that our method simultaneously profile multiple attributes with better performance.
Understanding Natural Disasters As Risks In Supply Chain Management Through Web Data Analysis, Jimmy Ong, Zhaoxia Wang, Rick Siow Mong Goh, Xiao Feng Yin, Xin Xin, Xiuju Fu
Understanding Natural Disasters As Risks In Supply Chain Management Through Web Data Analysis, Jimmy Ong, Zhaoxia Wang, Rick Siow Mong Goh, Xiao Feng Yin, Xin Xin, Xiuju Fu
Research Collection School Of Computing and Information Systems
With the increasing trend of global outsourcing, companies are now facing ever more complexsupply chains. When a company operates over a large geographical area, the likelihood of disruptions ispotentially increased due to such unforeseen events as natural disasters, union strikes or social unrest. Inthis paper, we consider natural disasters as a form of risks in supply chains and propose to aid itsmanagement by analyzing Web data collected in real-time. Using Twitter "tweets" as our primary source ofWeb data, a real-time data crawler is developed to collect and analyze tweets that are identified as relevant tonatural disasters. In addition, a visualization …
Nirmal: Automatic Identification Of Software Relevant Tweets Leveraging Language Model, Abishek Sharma, Yuan Tian, David Lo
Nirmal: Automatic Identification Of Software Relevant Tweets Leveraging Language Model, Abishek Sharma, Yuan Tian, David Lo
Research Collection School Of Computing and Information Systems
Twitter is one of the most widely used social media platforms today. It enables users to share and view short 140-character messages called 'tweets'. About 284 million active users generate close to 500 million tweets per day. Such rapid generation of user generated content in large magnitudes results in the problem of information overload. Users who are interested in information related to a particular domain have limited means to filter out irrelevant tweets and tend to get lost in the huge amount of data they encounter. A recent study by Singer et al. found that software developers use Twitter to …
Use Of A High-Value Social Audience Index For Target Audience Identification On Twitter, Siaw Ling Lo, David Cornforth, Raymond. Chiong
Use Of A High-Value Social Audience Index For Target Audience Identification On Twitter, Siaw Ling Lo, David Cornforth, Raymond. Chiong
Research Collection School Of Computing and Information Systems
With the large and growing user base of social media, it is not an easy feat to identify potential customers for business. This is mainly due to the challenge of extracting commercially viable contents from the vast amount of free-form conversations. In this paper, we analyse the Twitter content of an account owner and its list of followers through various text mining methods and segment the list of followers via an index. We have termed this index as the High-Value Social Audience (HVSA) index. This HVSA index enables a company or organisation to devise their marketing and engagement plan according …
Bridging The Vocabulary Gap Between Health Seekers And Healthcare Knowledge, Liqiang Nie, Yiliang Zhao, Akbari Mohammad, Jialie Shen, Tat-Seng Chua
Bridging The Vocabulary Gap Between Health Seekers And Healthcare Knowledge, Liqiang Nie, Yiliang Zhao, Akbari Mohammad, Jialie Shen, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
The vocabulary gap between health seekers and providers has hindered the cross-system operability and the interuser reusability. To bridge this gap, this paper presents a novel scheme to code the medical records by jointly utilizing local mining and global learning approaches, which are tightly linked and mutually reinforced. Local mining attempts to code the individual medical record by independently extracting the medical concepts from the medical record itself and then mapping them to authenticated terminologies. A corpus-aware terminology vocabulary is naturally constructed as a byproduct, which is used as the terminology space for global learning. Local mining approach, however, may …
Toward Mobile Robots Reasoning Like Humans, Jean Oh, Arne Suppe, Felix Duvallet, Abdeslam Boularias, Luis Navarro-Serment, Martial Hebert, Anthony Stentz, Jerry Vinokurov, Oscar Romero, Christian Lebiere, Robert Dean
Toward Mobile Robots Reasoning Like Humans, Jean Oh, Arne Suppe, Felix Duvallet, Abdeslam Boularias, Luis Navarro-Serment, Martial Hebert, Anthony Stentz, Jerry Vinokurov, Oscar Romero, Christian Lebiere, Robert Dean
Research Collection School Of Computing and Information Systems
Robots are increasingly becoming key players in human-robot teams. To become effective teammates, robots must possess profound understanding of an environment, be able to reason about the desired commands and goals within a specific context, and be able to communicate with human teammates in a clear and natural way. To address these challenges, we have developed an intelligence architecture that combines cognitive components to carry out high-level cognitive tasks, semantic perception to label regions in the world, and a natural language component to reason about the command and its relationship to the objects in the world. This paper describes recent …
Integrated Intelligence For Human-Robot Teams, Jean Oh, Et. Al.
Integrated Intelligence For Human-Robot Teams, Jean Oh, Et. Al.
Research Collection School Of Computing and Information Systems
With recent advances in robotics technologies and autonomous systems, the idea of human-robot teams is gaining ever-increasing attention. In this context, our research focuses on developing an intelligent robot that can autonomously perform non-trivial, but specific tasks conveyed through natural language. Toward this goal, a consortium of researchers develop and integrate various types of intelligence into mobile robot platforms, including cognitive abilities to reason about high-level missions, perception to classify regions and detect relevant objects in an environment, and linguistic abilities to associate instructions with the robot’s world model and to communicate with human teammates in a natural way. This …
Effects Of Training Datasets On Both The Extreme Learning Machine And Support Vector Machine For Target Audience Identification On Twitter, Siaw Ling Lo, David Cornforth, Raymond Chiong
Effects Of Training Datasets On Both The Extreme Learning Machine And Support Vector Machine For Target Audience Identification On Twitter, Siaw Ling Lo, David Cornforth, Raymond Chiong
Research Collection School Of Computing and Information Systems
The ability to identify or predict a target audience from the increasingly crowded social space will provide a company some competitive advantage over other companies. In this paper, we analyze various training datasets, which include Twitter contents of an account owner and its list of followers, using features generated in different ways for two machine learning approaches - the Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Various configurations of the ELM and SVM have been evaluated. The results indicate that training datasets using features generated from the owner tweets achieve the best performance, relative to other feature sets. …
Extracting Interest Tags From Twitter User Biographies, Ying Ding, Jing Jiang
Extracting Interest Tags From Twitter User Biographies, Ying Ding, Jing Jiang
Research Collection School Of Computing and Information Systems
Twitter, one of the most popular social media platforms, has been studied from different angles. One of the important sources of information in Twitter is users’ biographies, which are short self-introductions written by users in free form. Biographies often describe users’ background and interests. However, to the best of our knowledge, there has not been much work trying to extract information from Twitter biographies. In this work, we study how to extract information revealing users’ personal interests from Twitter biographies. A sequential labeling model is trained with automatically constructed labeled data. The popular patterns expressing user interests are extracted and …
Anomaly Detection Through Enhanced Sentiment Analysis On Social Media Data, Zhaoxia Wang, Victor Joo, Chuan Tong, Xin Xin, Hoong Chor Chin
Anomaly Detection Through Enhanced Sentiment Analysis On Social Media Data, Zhaoxia Wang, Victor Joo, Chuan Tong, Xin Xin, Hoong Chor Chin
Research Collection School Of Computing and Information Systems
Anomaly detection in sentiment analysis refers to detecting abnormal opinions, sentiment patterns or special temporal aspects of such patterns in a collection of data. The anomalies detected may be due to sudden sentiment changes hidden in large amounts of text. If these anomalies are undetected or poorly managed, the consequences may be severe, e.g. A business whose customers reveal negative sentiments and will no longer support the establishment. Social media platforms, such as Twitter, provide a vast source of information, which includes user feedback, opinion and information on most issues. Many organizations also leverage social media platforms to publish information …
Identifying The High-Value Social Audience From Twitter Through Text-Mining Methods, Siaw Ling Lo, David Cornforth, Raymond Chiong
Identifying The High-Value Social Audience From Twitter Through Text-Mining Methods, Siaw Ling Lo, David Cornforth, Raymond Chiong
Research Collection School Of Computing and Information Systems
Doing business on social media has become a common practice for many companies these days. While the contents shared on Twitter and Facebook offer plenty of opportunities to uncover business insights, it remains a challenge to sift through the huge amount of social media data and identify the potential social audience who is highly likely to be interested in a particular company. In this paper, we analyze the Twitter content of an account owner and its list of followers through various text mining methods, which include fuzzy keyword matching, statistical topic modeling and machine learning approaches. We use tweets of …
Linguistic Analysis Of Toxic Behavior In An Online Video Game, Haewoon Kwak, Telefonica
Linguistic Analysis Of Toxic Behavior In An Online Video Game, Haewoon Kwak, Telefonica
Research Collection School Of Computing and Information Systems
In this paper we explore the linguistic components of toxic behavior by using crowdsourced data from over 590 thousand cases of accused toxic players in a popular match-based competition game, League of Legends. We perform a series of linguistic analyses to gain a deeper understanding of the role communication plays in the expression of toxic behavior. We characterize linguistic behavior of toxic players and compare it with that of typical players in an online competition game. We also find empirical support describing how a player transitions from typical to toxic behavior. Our findings can be helpful to automatically detect and …
Vireo-Tno @ Trecvid 2014: Multimedia Event Detection And Recounting (Med And Mer), Chong-Wah Ngo, Yi-Jie Lu, Hao Zhang, Ting Yao, Chun-Chet Tan, Lei Pang, Maaike De Boer, John Schavemaker, Klamer Schutte, Wessel Kraaij
Vireo-Tno @ Trecvid 2014: Multimedia Event Detection And Recounting (Med And Mer), Chong-Wah Ngo, Yi-Jie Lu, Hao Zhang, Ting Yao, Chun-Chet Tan, Lei Pang, Maaike De Boer, John Schavemaker, Klamer Schutte, Wessel Kraaij
Research Collection School Of Computing and Information Systems
This paper presents an overview and comparative analysis of our systems designed for TRECVID 2014 [1] multimedia event detection (MED) and recounting (MER) tasks, including all sub-tasks for Pre-Specified (PS) event detection, all sub-tasks except 100Ex for Ad-Hoc (AH) event detection, and 010Ex sub-task for both PS and AH event recounting. Multimedia Event Detection (MED) : Our main focus for the MED task is on the study of a new zero-example system, which aims to solve the 000Ex and SQ problems. The system can run either fully automatically or semi-automatically. Specifically, we test the automatic run in 000Ex submission and …