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Physical Sciences and Mathematics Commons

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Databases and Information Systems

Series

2016

Semantics

Articles 1 - 3 of 3

Full-Text Articles in Physical Sciences and Mathematics

Pairwise Relation Classification With Mirror Instances And A Combined Convolutional Neural Network, Jianfei Yu, Jing Jiang Dec 2016

Pairwise Relation Classification With Mirror Instances And A Combined Convolutional Neural Network, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

Relation classification is the task of classifying the semantic relations between entity pairs in text. Observing that existing work has not fully explored using different representations for relation instances, especially in order to better handle the asymmetry of relation types, in this paper, we propose a neural network based method for relation classification that combines the raw sequence and the shortest dependency path representations of relation instances and uses mirror instances to perform pairwise relation classification. We evaluate our proposed models on two widely used datasets: SemEval-2010 Task 8 and ACE-2005. The empirical results show that our combined model together …


Can Instagram Posts Help Characterize Urban Micro-Events?, Kasthuri Jayarajah, Archan Misra Jul 2016

Can Instagram Posts Help Characterize Urban Micro-Events?, Kasthuri Jayarajah, Archan Misra

Research Collection School Of Computing and Information Systems

Social media content, from platforms such as Twitter and Foursquare, has enabled an exciting new field of social sensing, where participatory content generated by users has been used to identify unexpected emerging or trending events. In contrast to such text-based channels, we focus on image-sharing social applications (specifically Instagram), and investigate how such urban social sensing can leverage upon the additional multi-modal, multimedia content. Given the significantly higher fraction of geotagged content on Instagram, we aim to use such channels to go beyond identification of long-lived events (e.g., a marathon) to achieve finer-grained characterization of multiple micro-events (e.g., a person …


Semantic Proximity Search On Graphs With Metagraph-Based Learning, Yuan Fang, Wenqing Lin, Vincent W. Zheng, Min Wu, Kevin Chen-Chuan Chang, Xiao-Li Li May 2016

Semantic Proximity Search On Graphs With Metagraph-Based Learning, Yuan Fang, Wenqing Lin, Vincent W. Zheng, Min Wu, Kevin Chen-Chuan Chang, Xiao-Li Li

Research Collection School Of Computing and Information Systems

Given ubiquitous graph data such as the Web and social networks, proximity search on graphs has been an active research topic. The task boils down to measuring the proximity between two nodes on a graph. Although most earlier studies deal with homogeneous or bipartite graphs only, many real-world graphs are heterogeneous with objects of various types, giving rise to different semantic classes of proximity. For instance, on a social network two users can be close for different reasons, such as being classmates or family members, which represent two distinct classes of proximity. Thus, it becomes inadequate to only measure a …