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We present a novel approach to semi-supervised learning for text classification based on the higher-order co-occurrence paths of words. We name the proposed method as Semi-Supervised Semantic Higher-Order Smoothing (S3HOS). The S3HOS is built on a tri-partite graph based data representation of labeled and unlabeled documents that allows semantics in higher-order co-occurrence paths between terms (words)...
This paper describes a rule-based sentiment analysis algorithm for polarity classification of financial news articles. The system utilizes a prior polarity lexicon to classify the financial news articles into positive or negative. Sentiment composition rules are used to determine the polarity of each sentence in the news article, while the Positivity/Negativity ratio (P/N ratio) is used to calculate...
The overwhelming proliferation of digital images on media sharing webs have triggered the requirement of effective tools to retrieve images of interest using semantic concepts. Due to the semantic gap between low-level visual features and high-level semantic concepts of an image, however, the performances of many existing automatic image annotation algorithms are not so satisfactory. In this paper,...
Ontology integration is a well-known problem, a crucial mechanism for semantic interoperability and knowledge reusing, and a backbone of Semantic Web. In this paper, a graph-based method, which combines similarity flooding and concept classification for ontology integration, is proposed. This method consists of three main steps: model ontologies into directed labeled graph, concept classification,...
Agents embedded in open, dynamic and decentralized environments adopt different ontologies to describe their domain of discourse. Yet, agents have no prior knowledge of the other agents with whom they will interact. Therefore, a consistent and compatible communication relies on the agents' ability to reconcile in run-time the vocabulary used in their ontologies whose result is a set of correspondences...
This paper presents an empirical study of multi-label classification methods, and gives suggestions for multi-label classification that are effective for automatic image annotation applications. The study shows that triple random ensemble multi-label classification algorithm (TREMLC) outperforms among its counterparts, especially on scene image dataset. Multi-label k-nearest neighbor (ML-kNN) and...
Sentiment categorization have been widely explored in many fields, such as government policy, information monitoring, product tracking, etc. This paper adopts k-NN, Naive Bayes and SVM classifiers to categorize sentiments contained in on-line Chinese reviews on digital products. Our experimental results show that combining the words and phrases with sentiment orientation as hybrid features, SWM classifier...
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