• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.5
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• pp.454-462
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• 2016

portable white space device(WSD) obeying the Korean regulations of TV white space(TVWS) can cause harmful interference to a digital TV receiver residing at the same pixel around the edge of the digital TV service coverage for the case with a changed propagation environment. In order to solve this problem, we propose a method to allocate the resource of a hybrid WSD based on TVWS geo-location DB with spectrum sensing. Using the received power of digital TV signal through the spectrum sensing, a hybrid WSD can calculate the maximum permitted EIRP based on location probability. Based on the accurate allocation method proposed in this paper, it is possible to satisfy the Korean TVWS regulations and to eliminate the harmful interference to TV receivers nearby the hybrid WSD.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1307-1312
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• 2004

Word sense disambiguation is one of the most important problems in natural language processing research topics such as information retrieval and machine translation. Many approaches can be employed to resolve word ambiguity with a reasonable degree of accuracy. These strategies are: knowledge-based, corpus-based, and hybrid-based. This paper pays attention to the corpus-based strategy. The purpose of this paper is to compare three famous machine learning techniques, Snow, SVM and Naive Bayes in Word-Sense Disambiguation on Thai language. 10 ambiguous words are selected to test with word and POS features. The results show that SVM algorithm gives the best results in solving of Thai WSD and the accuracy rate is approximately 83-96%.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.2
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• pp.102-110
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• 2017

Current word sense disambiguation techniques employ various machine learning-based methods. Various approaches have been proposed to address this problem, including the knowledge base approach. This approach defines the sense of an ambiguous word in accordance with knowledge base information with no training corpus. In unsupervised learning techniques that use a knowledge base approach, graph-based and similarity-based methods have been the main research areas. The graph-based method has the advantage of constructing a semantic graph that delineates all paths between different senses that an ambiguous word may have. However, unnecessary semantic paths may be introduced, thereby increasing the risk of errors. To solve this problem and construct a fine-grained graph, in this paper, we propose a model that iteratively constructs the graph while eliminating unnecessary nodes and edges, i.e., senses and semantic paths. The hybrid similarity estimation model was applied to estimate a more accurate sense in the constructed semantic graph. Because the proposed model uses BabelNet, a multilingual lexical knowledge base, the model is not limited to a specific language.