• Interdisciplinary Bio Central
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• v.3 no.4
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• pp.14.1-14.9
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• 2011

Protein homology detection is an important issue in comparative genomics. Because of the exponential growth of sequence databases, fast and efficient homology detection tools are urgently needed. Currently, for homology detection, sequence comparison methods using local alignment such as BLAST are generally used as they give a reasonable measure for sequence similarity. However, these methods have drawbacks in offering overall sequence similarity, especially in dealing with eukaryotic genomes that often contain many insertions and duplications on sequences. Also these methods do not provide the explicit models for speciation, thus it is difficult to interpret their similarity measure into homology detection. Here, we present a novel method based on Word Conservation Score (WCS) to address the current limitations of homology detection. Instead of counting each amino acid, we adopted the concept of 'Word' to compare sequences. WCS measures overall sequence similarity by comparing word contents, which is much faster than BLAST comparisons. Furthermore, evolutionary distance between homologous sequences could be measured by WCS. Therefore, we expect that sequence comparison with WCS is useful for the multiple-species-comparisons of large genomes. In the performance comparisons on protein structural classifications, our method showed a considerable improvement over BLAST. Our method found bigger micro-syntenic blocks which consist of orthologs with conserved gene order. By testing on various datasets, we showed that WCS gives faster and better overall similarity measure compared to BLAST.

• Journal of the Korean Geosynthetics Society
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• v.22 no.2
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• pp.47-54
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• 2023

In this paper, we propose the technique of optimal search keyword extraction and retrieval for news article classification. The proposed technique was verified as an example of identifying trends related to North Korean construction. A representative Korean media platform, BigKinds, was used to select sample articles and extract keywords. The extracted keywords were vectorized using word embedding and based on this, the similarity between the extracted keywords was examined through cosine similarity. In addition, words with a similarity of 0.5 or higher were clustered based on the top 10 frequencies. Each cluster was formed as 'OR' between keywords inside the cluster and 'AND' between clusters according to the search form of the BigKinds. As a result of the in-depth analysis, it was confirmed that meaningful articles appropriate for the original purpose were extracted. This paper is significant in that it is possible to classify news articles suitable for the user's specific purpose without modifying the existing classification system and search form.

• Journal of KIISE:Databases
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• v.36 no.6
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• pp.466-475
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• 2009

Considerable research in the field of ontology mapping has been done when information sharing and reuse becomes necessary by a variety of ontology development. Ontology mapping method consists of the lexical, structural, instance, and logical inference similarity computing. Lexical similarity computing used in most ontology mapping methods performs an ontology mapping by using the synonym set defined in the WordNet. In this paper, we define the Super Word Set including the hypenym, hyponym, holonym, and meronym set and propose an ontology mapping method using the Super Word Set. The results of experiments show that our method improves the performance by up to 12%, compared with previous ontology mapping method.

• The Journal of the Korea Contents Association
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• v.12 no.3
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• pp.34-43
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• 2012

Determining the similarity between two strings can be applied various area such as information retrieval, spell checker and spam filtering. Similarity calculation between Korean strings based on dynamic programming methods firstly requires a definition of the similarity between phonemes. However, existing methods have a limitation that they use manually set similarity scores. In this paper, we propose a method to automatically calculate inter-phoneme similarity from a given set of variant words using a PAM-like probabilistic model. Our proposed method first finds the pairs of similar words from a given word set, and derives derivation rules from text alignment results among the similar word pairs. Then, similarity scores are calculated from the frequencies of variations between different phonemes. As an experimental result, we show an improvement of 10.1%~14.1% and 8.1%~11.8% in terms of sensitivity compared with the simple match-mismatch scoring scheme and the manually set inter-phoneme similarity scheme, respectively, with a specificity of 77.2%~80.4%.

• Phonetics and Speech Sciences
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• v.7 no.4
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• pp.41-47
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• 2015

This paper examines whether the Global Vector model is applicable to Korean data as a universal learning algorithm. The main purpose of this study is to compare the global vector model (GloVe) with the word2vec models such as a continuous bag-of-words (CBOW) model and a skip-gram (SG) model. For this purpose, we conducted an experiment by employing an evaluation corpus consisting of 70 target words and 819 pairs of Korean words for word similarities and analogies, respectively. Results of the word similarity task indicated that the Pearson correlation coefficients of 0.3133 as compared with the human judgement in GloVe, 0.2637 in CBOW and 0.2177 in SG. The word analogy task showed that the overall accuracy rate of 67% in semantic and syntactic relations was obtained in GloVe, 66% in CBOW and 57% in SG.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1311-1314
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• 2003

In this paper, we propose a method to measure document similarity. First, we have exploited single-term method that extracts nouns by using a lexical analyzer as a preprocessing step to match one index to one noun. In spite of irrelevance between documents, possibility of increasing document similarity is high with this method. For this reason, a term-phrase method has been reported. This method constructs co-occurrence between two words as an index to measure document similarity. In this paper, we tried another method that combine these two methods to compensate the problems in these two methods. Six types of features are extracted from two input documents, and they are fed into a neural network to calculate the final value of document similarity. Reliability of our method has been proved by an experiment of document retrieval.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.509-516
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• 2004

Semantic interpretation of image is incomplete without some mechanism for understanding semantic content that is not directly visible. For this reason, human assisted content-annotation through natural language is an attachment of textual description to image. However, keyword-based retrieval is in the level of syntactic pattern matching. In other words, dissimilarity computation among terms is usually done by using string matching not concept matching. In this paper, we propose a method for computerized semantic similarity calculation In WordNet space. We consider the edge, depth, link type and density as well as existence of common ancestors. Also, we have introduced method that applied similarity measurement on semantic image retrieval. To combine wi#h the low level features, we use the spatial color distribution model. When tested on a image set of Microsoft's 'Design Gallery Line', proposed method outperforms other approach.

• The Journal of the Acoustical Society of Korea
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• v.30 no.8
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• pp.428-435
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• 2011

Many isolated word recognition systems may generate irrelevant words for recognition results because they use only acoustic information or small amount of language information. In this paper, I propose word similarity that is used for selecting (or removing) less common words from candidates by applying Levenshtein distance. Word similarity is obtained by using positional accuracy that reflects the frequency information along to character's alignment information. This paper also discusses various improving techniques of selection of disparate words. The methods include different loss values, phone accuracy based on confusion information, weights of candidates by ranking order and partial comparisons. Through experiments, I found that the proposed methods are effective for removing heterogeneous words without loss of performance.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.11-12
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• 2021

소비자가 남긴 댓글이나 상품평은 상품기획의 주요 정보가 될 수 있다. 본 논문에서는 버티컬 무소음 마우스 7,300개에 대한 온라인 댓글을 딥러닝 기술인 Word2Vec을 이용하여 유사도 분석을 시행하였다. 유사도 분석결과 클릭 키워드에 대한 장점으로 소리(.975), 버튼(.972), 무게(.971)가 분석되었으며 단점은 가볍다(.959)이었다. 이는 구매 상품에 대한 소비자의 의견, 태도, 성향 및 서비스에 대한 포괄적인 의견들을 데이터화 하여 상품의 특징을 분석할 수 있는 의미있는 과정 이라고 볼 수 있다. 상품기획 프로세스에 딥러닝 기술을 통한 소비자의 감성분석자료 포함시키는 전략을 적용한다면 상품기획의 시간과 비용투자의 경제성을 높일 수 있고 나아가 빠르게 변화하는 소비자의 요구사항을 적기에 반영할 수 있을 것으로 생각된다.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.221-227
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• 2021

In this paper, we conducted word-to-word similarity analysis of standardized datasets collected through web crawling for 10,000 Vertical Noise Mouses using Word2Vec, and made 92 students of computer engineering use the products presented for 5 days, and conducted self-report questionnaire analysis. The questionnaire analysis was conducted by collecting the words in the form of a narrative form and presenting and selecting the top 50 words extracted from the word frequency analysis and the word similarity analysis. As a result of analyzing the similarity of e-commerce user's product review, pain (.985) and design (.963) were analyzed as the advantages of click keywords, and the disadvantages were vertical (.985) and adaptation (.948). In the descriptive frequency analysis, the most frequently selected items were Vertical (123) and Pain (118). Vertical (83) and Pain (75) were selected for the advantages of selecting the long/demerit similar words, and adaptation (89) and buttons (72) were selected for the disadvantages. Therefore, it is expected that decision makers and product planners of medium and small enterprises can be used as important data for decision making when the method applied in this study is reflected as a new product development process and a review strategy of existing products.