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Research Collection School Of Computing and Information Systems

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Articles 2491 - 2520 of 3436

Full-Text Articles in Databases and Information Systems

Direction-Based Surrounder Queries For Mobile Recommendations, Xi Guo, Baihua Zheng, Yoshiharu Ishikawa, Yunjun Gao Oct 2011

Direction-Based Surrounder Queries For Mobile Recommendations, Xi Guo, Baihua Zheng, Yoshiharu Ishikawa, Yunjun Gao

Research Collection School Of Computing and Information Systems

Location-based recommendation services recommend objects to the user based on the user’s preferences. In general, the nearest objects are good choices considering their spatial proximity to the user. However, not only the distance of an object to the user but also their directional relationship are important. Motivated by these, we propose a new spatial query, namely a direction-based surrounder (DBS) query, which retrieves the nearest objects around the user from different directions. We define the DBS query not only in a two-dimensional Euclidean space E">EE but also in a road network R">RR . In the Euclidean space E" …

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Context-Aware Nearest Neighbor Query On Social Networks, Yazhe Wang, Baihua Zheng Oct 2011

Context-Aware Nearest Neighbor Query On Social Networks, Yazhe Wang, Baihua Zheng

Research Collection School Of Computing and Information Systems

Social networking has grown rapidly over the last few years, and social networks contain a huge amount of content. However, it can be not easy to navigate the social networks to find specific information. In this paper, we define a new type of queries, namely context-aware nearest neighbor (CANN) search over social network to retrieve the nearest node to the query node that matches the context specified. CANN considers both the structure of the social network, and the profile information of the nodes. We design ahyper-graph based index structure to support approximated CANN search efficiently.

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Location-Dependent Spatial Query Containment, Ken C. K. Lee, Brandon Unger, Baihua Zheng, Wang-Chien Lee Oct 2011

Location-Dependent Spatial Query Containment, Ken C. K. Lee, Brandon Unger, Baihua Zheng, Wang-Chien Lee

Research Collection School Of Computing and Information Systems

Nowadays, location-related information is highly accessible to mobile users via issuing Location-Dependent Spatial Queries (LDSQs) with respect to their locations wirelessly to Location-Based Service (LBS) servers. Due to the limited mobile device battery energy, scarce wireless bandwidth, and heavy LBS server workload, the number of LDSQs submitted over wireless channels to LBS servers for evaluation should be minimized as appropriate. In this paper, we exploit query containment techniques for LDSQs (called LDSQ containment) to enable mobile clients to determine whether the result of a new LDSQ Q′ is completely covered by that of another LDSQ Q previously answered by a …

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On Modeling Virality Of Twitter Content, Tuan Anh Hoang, Ee Peng Lim, Palakorn Achananuparp, Jing Jiang, Feida Zhu Oct 2011

On Modeling Virality Of Twitter Content, Tuan Anh Hoang, Ee Peng Lim, Palakorn Achananuparp, Jing Jiang, Feida Zhu

Research Collection School Of Computing and Information Systems

Twitter is a popular microblogging site where users can easily use mobile phones or desktop machines to generate short messages to be shared with others in realtime. Twitter has seen heavy usage in many recent international events including Japan earthquake, Iran election, etc. In such events, many tweets may become viral for different reasons. In this paper, we study the virality of socio-political tweet content in the Singapore’s 2011 general election (GE2011). We collected tweet data generated by about 20K Singapore users from 1 April 2011 till 12 May 2011, and the follow relationships among them. We introduce several quantitative …

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Using Social Annotations For Trend Discovery In Scientific Publications, Meiqun Hu, Ee Peng Lim, Jing Jiang Oct 2011

Using Social Annotations For Trend Discovery In Scientific Publications, Meiqun Hu, Ee Peng Lim, Jing Jiang

Research Collection School Of Computing and Information Systems

Social tags and citing documents are two forms of social annotations to scientific publications. These social annotations provide useful contextual and temporal information for the annotated work, which encapsulates the attention and interest of the annotators. In this work, we explore the use of social annotations for discovering trends in scientific publications. We propose a trend discovery process that employs trend estimation and trend selection and ranking for analyzing the emerging trends shown in the social annotation profiles. The proposed sigmoid trend estimator allows us to characterize and compare how much, when and how fast the trends emerge. To perform …

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Spectral Geometry Image: Image Based 3d Models For Digital Broadcasting Applications, Boon Seng Chew, Lap Pui Chau, Ying He, Dayong Wang, Steven C. H. Hoi Sep 2011

Spectral Geometry Image: Image Based 3d Models For Digital Broadcasting Applications, Boon Seng Chew, Lap Pui Chau, Ying He, Dayong Wang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

The use of 3D models for progressive transmission and broadcasting applications is an interesting challenge due to the nature and complexity of such content. In this paper, a new image format for the representation of 3D progressive model is proposed. The powerful spectral analysis is combined with the state of art Geometry Image(GI) to encode static 3D models into spectral geometry images(SGI) for robust 3D shape representation. Based on the 3D model's surface characteristics, SGI separated the geometrical image into low and high frequency layers to achieve effective Level of Details(LOD) modeling. For SGI, the connectivity data of the model …

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Privacy Beyond Single Sensitive Attribute, Yuan Fang, Mafruz Zaman Ashrafi, See Kiong Ng Sep 2011

Privacy Beyond Single Sensitive Attribute, Yuan Fang, Mafruz Zaman Ashrafi, See Kiong Ng

Research Collection School Of Computing and Information Systems

Publishing individual specific microdata has serious privacy implications. The k-anonymity model has been proposed to prevent identity disclosure from microdata, and the work on ℓ-diversity and t-closeness attempt to address attribute disclosure. However, most current work only deal with publishing microdata with a single sensitive attribute (SA), whereas real life scenarios often involve microdata with multiple SAs that may be multi-valued. This paper explores the issue of attribute disclosure in such scenarios. We propose a method called CODIP (Complete Disjoint Projections) that outlines a general solution to deal with the shortcomings in a naïve approach. We also introduce two measures, …

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An Efficient Adaptive Vortex Particle Method For Real-Time Smoke Simulation, Shengfeng He, Hon-Cheng Wong, Un-Hong Wong Sep 2011

An Efficient Adaptive Vortex Particle Method For Real-Time Smoke Simulation, Shengfeng He, Hon-Cheng Wong, Un-Hong Wong

Research Collection School Of Computing and Information Systems

Smoke simulation is one of the interesting topics in computer animation and it usually involves turbulence generation. Efficient generation of realistic turbulent flows becomes one of the challenges in smoke simulation. Vortex particle method, which is a hybrid method that combines grid-based and particle-based approaches, is often used for generating turbulent details. However, it may cause irrational artifacts due to its initial condition and vorticity forcing approach used. In this paper, a new vorticity forcing approach based on the spatial adaptive vorticity confinement is proposed to address this problem. In this approach, the spatial adaptive vorticity confinement force varies with …

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Structural Analysis Of The Hot Spots In The Binding Between H1n1 Ha And The 2di Antibody: Do Mutations Of H1n1 From 1918 To 2009 Affect Much On This Binding?, Qian Liu, Steven C. H. Hoi, Chinh T. T. Su, Zhenhua Li, Chee-Keong Kwoh, Limsoon Wong, Jinyan Li Sep 2011

Structural Analysis Of The Hot Spots In The Binding Between H1n1 Ha And The 2di Antibody: Do Mutations Of H1n1 From 1918 To 2009 Affect Much On This Binding?, Qian Liu, Steven C. H. Hoi, Chinh T. T. Su, Zhenhua Li, Chee-Keong Kwoh, Limsoon Wong, Jinyan Li

Research Collection School Of Computing and Information Systems

Worldwide and substantial mortality caused by the 2009 H1N1 influenza A has stimulated a new surge of research on H1N1 viruses. An epitope conservation has been learned in the HA1 protein that allows antibodies to cross-neutralize both 1918 and 2009 H1N1. However, few works have thoroughly studied the binding hot spots in those two antigen–antibody interfaces which are responsible for the antibody cross-neutralization. We apply predictive methods to identify binding hot spots at the epitope sites of the HA1 proteins and at the paratope sites of the 2D1 antibody. We find that the six mutations at the HA1's epitope from …

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When Recommendation Meets Mobile: Contextual And Personalised Recommendation On The Go, Jinfeng Zhuang, Tao Mei, Steven C. H. Hoi, Ying-Qing Xu, Shipeng Li Sep 2011

When Recommendation Meets Mobile: Contextual And Personalised Recommendation On The Go, Jinfeng Zhuang, Tao Mei, Steven C. H. Hoi, Ying-Qing Xu, Shipeng Li

Research Collection School Of Computing and Information Systems

Mobile devices are becoming ubiquitous. People use their phones as a personal concierge discovering and making decisions anywhere and anytime. Understanding user intent on the go therefore becomes important for task completion on the phone. While existing efforts have predominantly focused on understanding the explicit user intent expressed by a textual or voice query, this paper presents an approach to context-aware and personalized entity recommendation which understands the implicit intent without any explicit user input on the phone. The approach, highly motivated from a large-scale mobile click-through analysis, is able to rank both the entity types and the entities within …

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Enhancing Brand Equity Through Flow And Telepresence: A Comparison Of 2d And 3d Virtual Worlds, Fiona Fui-Hoon Nah, B. Eschenbrenner, D. Dewester Sep 2011

Enhancing Brand Equity Through Flow And Telepresence: A Comparison Of 2d And 3d Virtual Worlds, Fiona Fui-Hoon Nah, B. Eschenbrenner, D. Dewester

Research Collection School Of Computing and Information Systems

This research uses theories of flow, telepresence, positive emotions, and brand equity to examine the effect of using two-dimensional versus three-dimensional virtual world environments on telepresence, enjoyment, brand equity, and behavioral intention. The findings suggest that the 3D virtual world environment produces both positive and negative effects on brand equity when compared to the 2D environment. The positive effect of the 3D virtual world environment on brand equity occurs through telepresence, a specific aspect of flow, as well as enjoyment. The negative effect on brand equity can be explained using distraction-conflict theory in which attentional conflicts faced by users of …

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Information Quality On The World Wide Web: Development Of A Framework, J. Kandari, E. Jones, Fiona Fui-Hoon Nah, R. Bishu Sep 2011

Information Quality On The World Wide Web: Development Of A Framework, J. Kandari, E. Jones, Fiona Fui-Hoon Nah, R. Bishu

Research Collection School Of Computing and Information Systems

Data consumers are provided with easy online access to information on the World Wide Web. However, consumers face information quality problems in their quest for information. This paper focuses on the development of an instrument to measure information quality (IQ) on the World Wide Web from a user's perspective. Based on a comprehensive review of the literature, 20 important IQ frameworks were identified. These models, though varied in their approach and application, share a number of characteristics regarding their classifications of the attributes of quality. The paper identifies common dimensions that exist across the existing IQ frameworks in the literature …

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Mining Top-K Large Structural Patterns In A Massive Network, Feida Zhu, Qiang Qu, David Lo, Xifeng Yan, Jiawei Han, Philip S. Yu Sep 2011

Mining Top-K Large Structural Patterns In A Massive Network, Feida Zhu, Qiang Qu, David Lo, Xifeng Yan, Jiawei Han, Philip S. Yu

Research Collection School Of Computing and Information Systems

With ever-growing popularity of social networks, web and bio-networks, mining large frequent patterns from a single huge network has become increasingly important. Yet the existing pattern mining methods cannot offer the efficiency desirable for large pattern discovery. We propose Spider- Mine, a novel algorithm to efficiently mine top-K largest frequent patterns from a single massive network with any user-specified probability of 1 − ϵ. Deviating from the existing edge-by-edge (i.e., incremental) pattern-growth framework, SpiderMine achieves its efficiency by unleashing the power of small patterns of a bounded diameter, which we call “spiders”. With the spider structure, our approach adopts a …

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Ccrank: Parallel Learning To Rank With Cooperative Coevolution, Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady W. Lauw Aug 2011

Ccrank: Parallel Learning To Rank With Cooperative Coevolution, Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady W. Lauw

Research Collection School Of Computing and Information Systems

We propose CCRank, the first parallel algorithm for learning to rank, targeting simultaneous improvement in learning accuracy and efficiency. CCRank is based on cooperative coevolution (CC), a divide-and-conquer framework that has demonstrated high promise in function optimization for problems with large search space and complex structures. Moreover, CC naturally allows parallelization of sub-solutions to the decomposed subproblems, which can substantially boost learning efficiency. With CCRank, we investigate parallel CC in the context of learning to rank. Extensive experiments on benchmarks in comparison with the state-of-the-art algorithms show that CCRank gains in both accuracy and efficiency.

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A Hubel Wiesel Model Of Early Concept Generalization Based On Local Correlation Of Input Features, Sepideh Sadeghi, Kiruthika Ramanathan Aug 2011

A Hubel Wiesel Model Of Early Concept Generalization Based On Local Correlation Of Input Features, Sepideh Sadeghi, Kiruthika Ramanathan

Research Collection School Of Computing and Information Systems

Hubel Wiesel models, successful in visual processing algorithms, have only recently been used in conceptual representation. Despite the biological plausibility of a Hubel-Wiesel like architecture for conceptual memory and encouraging preliminary results, there is no implementation of how inputs at each layer of the hierarchy should be integrated for processing by a given module, based on the correlation of the features. In our paper, we propose the input integration framework - a set of operations performed on the inputs to the learning modules of the Hubel Wiesel model of conceptual memory. These operations weight the modules as being general or …

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Relevant Knowledge Helps In Choosing Right Teacher: Active Query Selection For Ranking Adaptation, Peng Cai, Wei Gao, Kam-Fai Wong, Aoying Zhou Jul 2011

Relevant Knowledge Helps In Choosing Right Teacher: Active Query Selection For Ranking Adaptation, Peng Cai, Wei Gao, Kam-Fai Wong, Aoying Zhou

Research Collection School Of Computing and Information Systems

Learning to adapt in a new setting is a common challenge to our knowledge and capability. New life would be easier if we actively pursued supervision from the right mentor chosen with our relevant but limited prior knowledge. This variant principle of active learning seems intuitively useful to many domain adaptation problems. In this paper, we substantiate its power for advancing automatic ranking adaptation, which is important in web search since it's prohibitive to gather enough labeled data for every search domain for fully training domain-specific rankers. For the cost-effectiveness, it is expected that only those most informative instances in …

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Effects Of Mentoring On Player Performance In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Kuo-Wei Hsu, Jaideep Srivastava Jul 2011

Effects Of Mentoring On Player Performance In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Kuo-Wei Hsu, Jaideep Srivastava

Research Collection School Of Computing and Information Systems

Massively Multiplayer Online Role-Playing Games (MMORPGs) have become increasingly popular and have communities comprising millions of subscribers. With their increasing popularity, researchers are realizing that video games can be a means to fully observe an entire isolated universe. In this study, we examine and report our findings on the effects of mentoring activities on player performance in Ever Quest II, a popular MMORPG developed by Sony Online Entertainment.

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Heuristic Algorithms For Balanced Multi-Way Number Partitioning, Jilian Zhang, Kyriakos Mouratidis, Hwee Hwa Pang Jul 2011

Heuristic Algorithms For Balanced Multi-Way Number Partitioning, Jilian Zhang, Kyriakos Mouratidis, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

Balanced multi-way number partitioning (BMNP) seeks to split a collection of numbers into subsets with (roughly) the same cardinality and subset sum. The problem is NP-hard, and there are several exact and approximate algorithms for it. However, existing exact algorithms solve only the simpler, balanced two-way number partitioning variant, whereas the most effective approximate algorithm, BLDM, may produce widely varying subset sums. In this paper, we introduce the LRM algorithm that lowers the expected spread in subset sums to one third that of BLDM for uniformly distributed numbers and odd subset cardinalities. We also propose Meld, a novel strategy for …

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Trust Network Inference For Online Rating Data Using Generative Models, Freddy Tat Chua Chua, Ee Peng Lim Jul 2011

Trust Network Inference For Online Rating Data Using Generative Models, Freddy Tat Chua Chua, Ee Peng Lim

Research Collection School Of Computing and Information Systems

In an online rating system, raters assign ratings to objects contributed by other users. In addition, raters can develop trust and distrust on object contributors depending on a few rating and trust related factors. Previous study has shown that ratings and trust links can influence each other but there has been a lack of a formal model to relate these factors together. In this paper, we therefore propose Trust Antecedent Factor (TAF)Model, a novel probabilistic model that generate ratings based on a number of rater’s and contributor’s factors. We demonstrate that parameters of the model can be learnt by Collapsed …

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A Hybrid Agent Architecture Integrating Desire, Intention And Reinforcement Learning, Ah-Hwee Tan, Yew-Soon Ong, Akejariyawong Tapanuj Jul 2011

A Hybrid Agent Architecture Integrating Desire, Intention And Reinforcement Learning, Ah-Hwee Tan, Yew-Soon Ong, Akejariyawong Tapanuj

Research Collection School Of Computing and Information Systems

This paper presents a hybrid agent architecture that integrates the behaviours of BDI agents, specifically desire and intention, with a neural network based reinforcement learner known as Temporal DifferenceFusion Architecture for Learning and COgNition (TD-FALCON). With the explicit maintenance of goals, the agent performs reinforcement learning with the awareness of its objectives instead of relying on external reinforcement signals. More importantly, the intention module equips the hybrid architecture with deliberative planning capabilities, enabling the agent to purposefully maintain an agenda of actions to perform and reducing the need of constantly sensing the environment. Through reinforcement learning, plans can also be …

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Generating Aspect-Oriented Multi-Document Summarization With Event-Aspect Model, Peng Li, Yinglin Wang, Wei Gao, Jing Jiang Jul 2011

Generating Aspect-Oriented Multi-Document Summarization With Event-Aspect Model, Peng Li, Yinglin Wang, Wei Gao, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we propose a novel approach to automatic generation of aspect-oriented summaries from multiple documents. We first develop an event-aspect LDA model to cluster sentences into aspects. We then use extended LexRank algorithm to rank the sentences in each cluster. We use Integer Linear Programming for sentence selection. Key features of our method include automatic grouping of semantically related sentences and sentence ranking based on extension of random walk model. Also, we implement a new sentence compression algorithm which use dependency tree instead of parser tree. We compare our method with four baseline methods. Quantitative evaluation based on …

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Online Auc Maximization, Peilin Zhao, Steven C. H. Hoi, Rong Jin, Tianbo Yang Jul 2011

Online Auc Maximization, Peilin Zhao, Steven C. H. Hoi, Rong Jin, Tianbo Yang

Research Collection School Of Computing and Information Systems

Most studies of online learning measure the performance of a learner by classification accuracy, which is inappropriate for applications where the data are unevenly distributed among different classes. We address this limitation by developing online learning algorithm for maximizing Area Under the ROC curve (AUC), a metric that is widely used for measuring the classification performance for imbalanced data distributions. The key challenge of online AUC maximization is that it needs to optimize the pairwise loss between two instances from different classes. This is in contrast to the classical setup of online learning where the overall loss is a sum …

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Mining Weakly Labeled Web Facial Images For Search-Based Face Annotation, Dayang Wang, Steven C. H. Hoi, Ying He Jul 2011

Mining Weakly Labeled Web Facial Images For Search-Based Face Annotation, Dayang Wang, Steven C. H. Hoi, Ying He

Research Collection School Of Computing and Information Systems

In this paper, we investigate a search-based face annotation framework by mining weakly labeled facial images that are freely available on the internet. A key component of such a search-based annotation paradigm is to build a database of facial images with accurate labels. This is however challenging since facial images on the WWW are often noisy and incomplete. To improve the label quality of raw web facial images, we propose an effective Unsupervised Label Refinement (ULR) approach for refining the labels of web facial images by exploring machine learning techniques. We develop effective optimization algorithms to solve the large-scale learning …

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Parallel Learning To Rank For Information Retrieval, Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady W. Lauw Jul 2011

Parallel Learning To Rank For Information Retrieval, Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Learning to rank represents a category of effective ranking methods for information retrieval. While the primary concern of existing research has been accuracy, learning efficiency is becoming an important issue due to the unprecedented availability of large-scale training data and the need for continuous update of ranking functions. In this paper, we investigate parallel learning to rank, targeting simultaneous improvement in accuracy and efficiency.

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Unsupervised Discovery Of Discourse Relations For Eliminating Intra-Sentence Polarity Ambiguities, Lanjun Zhou, Binyang Li, Wei Gao, Zhongyu Wei, Kam-Fai Wong Jul 2011

Unsupervised Discovery Of Discourse Relations For Eliminating Intra-Sentence Polarity Ambiguities, Lanjun Zhou, Binyang Li, Wei Gao, Zhongyu Wei, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Polarity classification of opinionated sentences with both positive and negative sentiments1 is a key challenge in sentiment analysis. This paper presents a novel unsupervised method for discovering intra-sentence level discourse relations for eliminating polarity ambiguities. Firstly, a discourse scheme with discourse constraints on polarity was defined empirically based on Rhetorical Structure Theory (RST). Then, a small set of cuephrase-based patterns were utilized to collect a large number of discourse instances which were later converted to semantic sequential representations (SSRs). Finally, an unsupervised method was adopted to generate, weigh and filter new SSRs without cue phrases for recognizing discourse relations. Experimental …

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Unsupervised Information Extraction With Distributional Prior Knowledge, Cane Wing-Ki Leung, Jing Jiang, Kian Ming A. Chai, Hai Leong Chieu, Loo-Nin Teow Jul 2011

Unsupervised Information Extraction With Distributional Prior Knowledge, Cane Wing-Ki Leung, Jing Jiang, Kian Ming A. Chai, Hai Leong Chieu, Loo-Nin Teow

Research Collection School Of Computing and Information Systems

We address the task of automatic discovery of information extraction template from a given text collection. Our approach clusters candidate slot fillers to identify meaningful template slots. We propose a generative model that incorporates distributional prior knowledge to help distribute candidates in a document into appropriate slots. Empirical results suggest that the proposed prior can bring substantial improvements to our task as compared to a K-means baseline and a Gaussian mixture model baseline. Specifically, the proposed prior has shown to be effective when coupled with discriminative features of the candidates.

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Linking Entities To A Knowledge Base With Query Expansion, Swapna Gottipati, Jing Jiang Jul 2011

Linking Entities To A Knowledge Base With Query Expansion, Swapna Gottipati, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper we present a novel approach to entity linking based on a statistical language model-based information retrieval with query expansion. We use both local contexts and global world knowledge to expand query language models. We place a strong emphasis on named entities in the local contexts and explore a positional language model to weigh them differently based on their distances to the query. Our experiments on the TAC-KBP 2010 data show that incorporating such contextual information indeed aids in disambiguating the named entities and consistently improves the entity linking performance. Compared with the official results from KBP 2010 …

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Generating Aspect-Oriented Multi-Document Summarization With Event-Aspect Model, Peng Li, Yinglin Wang, Wei Gao, Jing Jiang Jul 2011

Generating Aspect-Oriented Multi-Document Summarization With Event-Aspect Model, Peng Li, Yinglin Wang, Wei Gao, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we propose a novel approach to automatic generation of aspect-oriented summaries from multiple documents. We first develop an event-aspect LDA model to cluster sentences into aspects. We then use extended LexRank algorithm to rank the sentences in each cluster. We use Integer Linear Programming for sentence selection. Key features of our method include automatic grouping of semantically related sentences and sentence ranking based on extension of random walk model. Also, we implement a new sentence compression algorithm which use dependency tree instead of parser tree. We compare our method with four baseline methods. Quantitative evaluation based on …

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Continuous Visible Nearest Neighbor Query Processing In Spatial Databases, Yunjun Gao, Baihua Zheng, Gencai Chen, Qing Li, Xiaofa Guo Jun 2011

Continuous Visible Nearest Neighbor Query Processing In Spatial Databases, Yunjun Gao, Baihua Zheng, Gencai Chen, Qing Li, Xiaofa Guo

Research Collection School Of Computing and Information Systems

In this paper, we identify and solve a new type of spatial queries, called continuous visible nearest neighbor (CVNN) search. Given a data set P, an obstacle set O, and a query line segment q in a two-dimensional space, a CVNN query returns a set of $${\langle p, R\rangle}$$ tuples such that $${p \in P}$$ is the nearest neighbor to every point r along the interval $${R \subseteq q}$$ as well as pis visible to r. Note that p may be NULL, meaning that all points in P are invisible to all points in R due to the obstruction of …

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Link Type Based Pre-Cluster Pair Model For Coreference Resolution, Yang Song, Houfeng Wang, Jing Jiang Jun 2011

Link Type Based Pre-Cluster Pair Model For Coreference Resolution, Yang Song, Houfeng Wang, Jing Jiang

Research Collection School Of Computing and Information Systems

This paper presents our participation in the CoNLL-2011 shared task, Modeling Unrestricted Coreference in OntoNotes. Coreference resolution, as a difficult and challenging problem in NLP, has attracted a lot of attention in the research community for a long time. Its objective is to determine whether two mentions in a piece of text refer to the same entity. In our system, we implement mention detection and coreference resolution seperately. For mention detection, a simple classification based method combined with several effective features is developed. For coreference resolution, we propose a link type based pre-cluster pair model. In this model, pre-clustering of …

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