Wei Liu

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Wei Liu is an author.

Publications

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Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Integrating visual classifier ensemble with term extraction for Automatic Image Annotation Proceedings of the 2011 6th IEEE Conference on Industrial Electronics and Applications, ICIEA 2011 English 2011 Existing Automatic Image Annotation (AIA) systems are typically developed, trained and tested using high quality, manually labelled images. The tremendous manual efforts required with an untested ability to scale and tolerate noise all have an impact on existing systems' applicability to real-world data. In this paper, we propose a novel AIA system which harnesses the collective intelligence on the Web to automatically construct training data to work with an ensemble of Support Vector Machine (SVM) classifiers based on Multi-Instance Learning (MIL) and global features. An evaluation of the proposed annotation approach using an automatically constructed training set from Wikipedia demonstrates a slight improvement of in annotation accuracy in comparison with two existing systems. 0 0
Merging of topic maps based on corpus Similarity calculation
Text simiarlity
Topic map merging
Proceedings - International Conference on Electrical and Control Engineering, ICECE 2010 English 2010 The distributed topic maps often need be merged when they are used for knowledge representation, the similarity calculation of two topics is a critical factor which affects the quality of final topic maps directly. In this paper, we present a novel approach to calculate the similarity of topics and merge the distributed topic maps, the method not only implements the syntax comparison between the topics, but constructs a domain-specific dictionary to resolve the low precision of topic semantic similarity calculation using the common dictionary purely, the massive texts are gathered form Wikipedia and Google snippets as corpus, on which the similarity score of the specific terms is calculated and stored to dictionary by a semantic text comparison method. The experiment indicates the new method can resolve particularly the problems of the common dictionary lacking many technical terms. 0 0
Wikipedia-Graph Based Key Concept Extraction towards News Analysis Key concept extraction
Wikipedia Concept Graph
Graph theory
CEC English 2009 0 0
Tree-traversing ant algorithm for term clustering based on featureless similarities Data Mining and Knowledge Discovery 2007 Many conventional methods for concepts formation in ontology learning have relied on the use of predefined templates and rules, and static resources such as {WordNet.} Such approaches are not scalable, difficult to port between different domains and incapable of handling knowledge fluctuations. Their results are far from desirable, either. In this paper, we propose a new ant-based clustering algorithm, {Tree-Traversing} Ant {(TTA),} for concepts formation as part of an ontology learning system. With the help of Normalized Google Distance {(NGD)} and n of Wikipedia {(nW)} as measures for similarity and distance between terms, we attempt to achieve an adaptable clustering method that is highly scalable and portable across domains. Evaluations with an seven datasets show promising results with an average lexical overlap of 97\% and ontological improvement of 48\%. At the same time, the evaluations demonstrated several advantages that are not simultaneously present in standard ant-based and other conventional clustering methods. 0 0
Featureless similarities for terms clustering using tree-traversing ants Ant-based clustering method
Featureless similarity measures
Terms clustering
ACM International Conference Proceeding Series English 2006 Besides being difficult to scale between different domains and to handle knowledge fluctuations, the results of terms clustering presented by existing ontology engineering systems are far from desirable. In this paper, we propose a new version of ant-based method for clustering terms known as Tree-Traversing Ants (TTA). With the help of the Normalized Google Distance (NGD) and n° of Wikipedia (n°W) as measures for similarity and distance between terms, we attempt to achieve an adaptable clustering method that is highly scalable across domains. Initial experiments with two datasets show promising results and demonstrated several advantages that are not simultaneously present in standard ant-based and other conventional clustering methods. Copyright 0 0