• Journal of the Korean Society for information Management
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• v.15 no.3
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• pp.7-26
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• 1998

The large scale documents of the data repository are utilized to the diverse applications. In the cross-language information retrieval, if the words of a query contain polymorphic meanings, the system needs multilingual corpus to exactly translate to the target words. We constructed the financial comparable corpus, called KFCM(Korean Financial Corpus corresponding to MLCC Corpus), comparing to the MLCC Polylingual Documents which consisted with the 6 European languages. It is independently constructed under the DTD of MLCC comparable corpus, and can be utilized to the cross-language information retrieval. In this paper, we discussed about the application and construction procedures of KFCM which is public domain data.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.805-817
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• 2017

Cross-lingual query expansion is usually based on the relationship among monolingual words. Bilingual comparable corpus contains relationships among bilingual words. Therefore, this paper proposes a method based on these relationships to conduct query expansion. First, the word vectors which characterize the bilingual words are trained using Chinese and Thai bilingual comparable corpus. Then, the correlation between Chinese query words and Thai words are computed based on these word vectors, followed with selecting the Thai candidate expansion terms via the correlative value. Then, multi-group Thai query expansion sentences are built by the Thai candidate expansion words based on Chinese query sentence. Finally, we can get the optimal sentence using the Chinese and Thai query expansion method, and perform the Thai query expansion. Experiment results show that the cross-lingual query expansion method we proposed can effectively improve the accuracy of Chinese and Thai cross-language information retrieval.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.3
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• pp.300-306
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• 2013

This paper presents a new method for constructing bilingual lexicons through a pivot language. The proposed method is adapted from the context-based approach, called the standard approach, which is well-known for building bilingual lexicons using comparable corpora. The main difference between the standard approach and the proposed method is how to represent context vectors. The former is to represent context vectors in a target language, while the latter in a pivot language. The proposed method is very simplified from the standard approach thereby. Furthermore, the proposed method is more accurate than the standard approach because it uses parallel corpora instead of comparable corpora. The experiments are conducted on a language pair, Korean and Spanish. Our experimental results have shown that the proposed method is quite attractive where a parallel corpus directly between source and target languages are unavailable, but both source-pivot and pivot-target parallel corpora are available.

• Phonetics and Speech Sciences
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• v.6 no.3
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• pp.139-145
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• 2014

The paper describes methods of conducting vowel analysis from a large-scale corpus with the aids of forced alignment and optimal formant ceiling methods. 'Read Style Corpus of Standard Korean' is used for building the forced alignment system and a subset of the corpus for the processing and extraction of features for vowel analysis based on optimal formant ceiling. The results of the vowel analysis are reliable and comparable to the results obtained using traditional analytical methods. The findings indicate that the methods adopted for the analysis can be extended and be used for more fine-grained analysis without time-consuming manual labeling without losing accuracy and reliability.

• Biomolecules & Therapeutics
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• v.11 no.4
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• pp.232-237
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• 2003

The present study examined functional effects of a new selective phosphodiesterase type 5 inhibitor, 1-[4-ethoxy-3-(6,7-dihydro-1-methyl-7-thioxo-3-propyl-1H-pyrazolo[ 4,3]pyrimidin-5-yl)phenylsulphonyl]-4-methyl piperazine (KJH-1002), in the isolated rabbit corpus cavernosum (RCC). Relaxing effects of KJH-1002 were also compared with those of sildenafil, which is currently used as an oral therapy for penile erectile dysfunction. In the isolated RCC precontracted with phenylephrine, both KJH-1002 and sildenafil in the concentration range of 1 to 1000 nM, produced a comparable potentiation of the electical field stimulation-induced relaxation in a concentration-dependent manner. In the sodium nitroprusside (SNP)-induced relaxation, the $IC_{50}$/ values, concentrations of SNP required to produce a 50％ relaxation of the phenylephrine-induced contraction, were significantly decreased to the similar extent by treatments with KJH-1002 and sildenafil. The results suggest that a new selective phosphodiesterase type 5 inhibitor, KJH-1002, has an augmentative effect on penile erection comparable to that of sildenafil and can be useful for the treatment of erectile dysfunction.

• Proceedings of the Korean Society for Language and Information Conference
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• 2002.02a
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• pp.79-91
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• 2002

Structural analysis of compound words is necessary and an important process in natural language processing. Proposed here is a corpus- and statistics- based method for the structural analysis of compound words in Japanese. We determine the structure of a compound word by using Internet corpus and calculating the strength of word association among its constituent words. Experiments with 5, 6, 7, and 8 kanji compound words show that our method works well and its performance is better than those of other comparable studies.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.5
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• pp.683-693
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• 2012

Recently, statistical machine translation (SMT) has received many attention with ease of its implementation and maintenance. The goal of our works is to build bidirectional Korean-Japanese SMT system by using MOSES [1] system. We use Korean-Japanese bilingual corpus which is aligned per sentence to train the translation model and use a large raw corpus in each language to train each language model. The proposed system shows results comparable to those of a rule-based machine translation system. Most of errors are caused by noises occurred in each processing stage.

• Journal of KIISE
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• v.42 no.7
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• pp.901-909
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• 2015

In this paper, to build a parallel corpus between Korean and English in Wikipedia. We proposed a method to find similar sentences based on language resources and topic modeling. We first applied language resources(Wiki-dictionary, numbers, and online dictionary in Daum) to match word sequentially. We construct the Wiki-dictionary using titles in Wikipedia. In order to take advantages of the Wikipedia, we used translation probability in the Wiki-dictionary for word matching. In addition, we improved the accuracy of sentence similarity measuring method by using word distribution based on topic modeling. In the experiment, a previous study showed 48.4% of F1-score with only language resources based on linear combination and 51.6% with the topic modeling considering entire word distributions additionally. However, our proposed methods with sequential matching added translation probability to language resources and achieved 9.9% (58.3%) better result than the previous study. When using the proposed sequential matching method of language resources and topic modeling after considering important word distributions, the proposed system achieved 7.5%(59.1%) better than the previous study.

• Cross-Cultural Studies
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• v.45
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• pp.109-143
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• 2016

This article examines distinctive linguistic shifts of translational English in an effort to verify the validity of the translation universals hypotheses, including simplification, explicitation, normalization and leveling-out, which have been most heavily explored to date. A large-scale study involving comparable corpora of translated and non-translated English newspaper texts has been carried out to typify particular linguistic attributes inherent in translated texts. The main findings are as follows. First, by employing the parameters of STTR, top-to-bottom frequency words, and mean values of sentence lengths, the translational instances of simplification have been detected across the translated English newspaper corpora. In contrast, the portion of function words produced contrary results, which in turn suggests that this feature might not constitute an effective test of the hypothesis. Second, it was found that the use of connectives was more salient in original English newspaper texts than translated English texts, being incompatible with the explicitation hypothesis. Third, as an indicator of translational normalization, lexical bundles were found to be more pervasive in translated texts than in non-translated texts, which is expected from and therefore support the normalization hypothesis. Finally, the standard deviations of both STTR and mean sentence lengths turned out to be higher in translated texts, indicating that the translated English newspaper texts were less leveled out within the same corpus group, which is opposed to what the leveling-out hypothesis postulates. Overall, the results suggest that not all four hypotheses may qualify for the label translation universals, or at least that some translational predictors are not feasible enough to evaluate the effectiveness of the translation universals hypotheses.

• ETRI Journal
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• v.35 no.1
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• pp.100-108
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• 2013

In this paper, a feature extraction (FE) method is proposed that is comparable to the traditional FE methods used in automatic speech recognition systems. Unlike the conventional spectral-based FE methods, the proposed method evaluates the similarities between an embedded speech signal and a set of predefined speech attractor models in the reconstructed phase space (RPS) domain. In the first step, a set of Gaussian mixture models is trained to represent the speech attractors in the RPS. Next, for a new input speech frame, a posterior-probability-based feature vector is evaluated, which represents the similarity between the embedded frame and the learned speech attractors. We conduct experiments for a speech recognition task utilizing a toolkit based on hidden Markov models, over FARSDAT, a well-known Persian speech corpus. Through the proposed FE method, we gain 3.11% absolute phoneme error rate improvement in comparison to the baseline system, which exploits the mel-frequency cepstral coefficient FE method.