• Journal of KIISE:Computer Systems and Theory
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• v.30 no.10
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• pp.586-594
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• 2003

X-Match algorithm is a lossless compression algorithm suitable for hardware implementation owing to its simplicity. It can compress 32 bits per clock cycle and is suitable for real time compression. However, as the bus width increases 64-bit, the compression unit also need to increase. This paper proposes the cooperative parallel X-Match (X-MatchCP) algorithm, which improves the compression speed by performing the two X-Match algorithms in parallel. It searches the all dictionary for two words, combines the compression codes of two words generated by parallel X-Match compression and outputs the combined code while the previous parallel X-Match algorithm searches an individual dictionary. The compression ratio in X-MatchCP is almost the same as in X-Match. X-MatchCP algorithm is described and simulated by Verilog hardware description language.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.9
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• pp.259-266
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• 2020

In this paper, we propose a novel scheme for product effect analysis, termed PEM, to find out the effectiveness of products used for improving the current condition, such as health supplements and cosmetics, by utilizing online customer reviews. The proposed technique preprocesses online customer reviews to remove advertisements automatically, constructs the word dictionary composed of symptoms, effects, increases, and decreases, and measures products' effects from online customer reviews. Using Naver Shopping Review datasets collected through crawling, we evaluated the performance of PEM compared to those of two methods using traditional sentiment dictionary and an RNN model, respectively. Our experimental results shows that the proposed technique outperforms the other two methods. In addition, by applying the proposed technique to the online customer reviews of atopic dermatitis and acne, effective treatments for them were found appeared on online social media. The proposed product effect analysis technique presented in this paper can be applied to various products and social media because it can score the effect of products from reviews of various media including blogs.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.1
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• pp.31-42
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• 2012

In order to process the suffix derived nouns of Korean, most of Korean processing systems have been registering the suffix derived nouns in dictionary. However, this approach is limited because the suffix is very high productive. Therefore, it is necessary to analyze semantically the unregistered suffix derived nouns. In this paper, we propose a method to disambiguate homograph suffixes using Korean lexical semantic network(U-WIN) for the purpose of semantic analysis of the suffix derived nouns. 33,104 suffix derived nouns including the homograph suffixes in the morphological and semantic tagged Sejong Corpus were used for experiments. For the experiments first of all we semantically tagged the homograph suffixes and extracted root of the suffix derived nouns and mapped the root to nodes in the U-WIN. And we assigned the distance weight to the nodes in U-WIN that could combine with each homograph suffix and we used the distance weight for disambiguating the homograph suffixes. The experiments for 35 homograph suffixes occurred in the Sejong corpus among 49 homograph suffixes in a Korean dictionary result in 91.01% accuracy.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.977-983
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• 1994

In this paper we have evaluated the synthetic speech quality by the proposed TD-PCULI speech synthesis method. For the synthesis we have extracted parameters from the Korean monosyllables through the analysis of speech waveforms in the time domain. We have constructed the Korean data format dictionary for the synthesis-by-rule depending upon the frequencies of the Korean pronunciation large vocabulary dictionary, in which V type syllables are 19, CV type's are 80, VC type's are 30 and CVC type's are 100. And using them we have synthesized various Korean monosyllables, words and sentences. We have tested each 10 syllables selected according to the 4 Korean syllable types with the objective MOS(Mean Opinion Score) evluation method about the 4 items i.e., intelligibility, clearness, loudness, and naturality after selecting random group without the knowledge of them. And also we have tested the possibility to modify a duration and F0 into another forms with changing a duration (i.e., 150msec, 300msec, 500msec, 700msec and 1sec) and a central fundamental frequency(i.e., 80Hz, 118Hz, 140Hz, 170Hz, and 200Hz). As the results of experiments the noises occurred in the course of synthesizing the speech by the rules are removed to be a very clear level and we can find that the prosodic elements can be controled as a good condition.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.179-186
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• 2021

We have been developed the smart tourism app and the Instagram and YouTube contents to provide personalized tourism information and travel product information to individual tourists. In this paper, we develop a rule-based smart tourism chatbot with the khaiii (Kakao Hangul Analyzer III) morphological analyzer and Neo4J graph database. In the proposed chatbot system, we use a morpheme analyzer, a proper noun dictionary including tourist destination names, and a general noun dictionary including containing frequently used words in tourist information search to understand the intention of the user's question. The tourism knowledge base built using the Neo4J graph database provides adequate answers to tourists' questions. In this paper, the nodes of Neo4J are Area based on tourist destination address, Contents with property of tourist information, and Service including service attribute data frequently used for search. A Neo4J query is created based on the result of analyzing the intention of a tourist's question with the property of nodes and relationships in Neo4J database. An answer to the question is made by searching in the tourism knowledge base. In this paper, we create the tourism knowledge base using more than 1300 Jeju tourism information used in the smart tourism app. We plan to develop a multilingual smart tour chatbot using the named entity recognition (NER), intention classification using conditional random field(CRF), and transfer learning using the pretrained language models.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.1-8
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• 2019

Among the methods for extracting the most appropriate information from a large amount of log data, there is a method using inductive inference. In this paper, we use SVM (Support Vector Machine), which is an excellent classification method for inductive inference, in order to determine the ranking of intrusion logs in digital forensic analysis. For this purpose, the logs of the training log set are classified into intrusion logs and normal logs. The associated words are extracted from each classified set to generate a related word dictionary, and each log is expressed as a vector based on the generated dictionary. Next, the logs are learned using the SVM. We classify test logs into normal logs and intrusion logs by using the log set extracted through learning. Finally, the recommendation orders of intrusion logs are determined to recommend intrusion logs to the forensic analyst.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.465-472
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• 2021

In this paper, we present an approach for detection of adverse drug reactions from drug reviews to compensate limitations of the spontaneous adverse drug reactions reporting system. Considering negative reviews usually contain adverse drug reactions, sentiment analysis on drug reviews was performed and extracted negative reviews. After then, MedDRA dictionary and named entity recognition were applied to the negative reviews to detect adverse drug reactions. For the experiment, drug reviews of Celecoxib, Naproxen, and Ibuprofen from 5 drug review sites, and analyzed. Our results showed that detection of adverse drug reactions is able to compensate to limitation of under-reporting in the spontaneous adverse drugs reactions reporting system.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.175-192
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• 2022

Artificial intelligence-based natural language processing technology is playing an important role in helping users write English-language documents. For academic documents in particular, the English proofreading services should reflect the academic characteristics using formal style and technical terms. But the services usually does not because they are based on general English sentences. In addition, since existing studies are mainly for improving the grammatical completeness, there is a limit of fluency improvement. This study proposes an automatic academic English editing methodology to deliver the clear meaning of sentences based on the use of technical terms. The proposed methodology consists of two phases: misspell correction and fluency improvement. In the first phase, appropriate corrective words are provided according to the input typo and contexts. In the second phase, the fluency of the sentence is improved based on the automatic post-editing model of the bidirectional recurrent neural network that can learn from the pair of the original sentence and the edited sentence. Experiments were performed with actual English editing data, and the superiority of the proposed methodology was verified.

• Information Systems Review
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• v.26 no.1
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• pp.269-288
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• 2024

The main objective of this study is to empirically explore how the organizational culture influences financial performance of companies. To achieve this, 58 companies included in the KOSPI 200 were selected from an online job platform in South Korea, JobPlanet. In order to understand the organizational culture of these companies, data was collected and analyzed from 81,067 reviews written by current and former members of these companies on JobPlanet over a period of 9 years from 2014 to 2022. To define the organizational culture of each company based on the review data, this study utilized well-known text analysis techniques, namely Word2Vec and FastText analysis methods. By modifying, supplementing, and extending the keywords associated with the five organizational culture values (Innovation, Integrity, Quality, Respect, and Teamwork) defined by Guiso et al. (2015), this study created a new Culture Dictionary. By using this dictionary, this study explored which cultural values-related keywords appear most often in the review data of each company, revealing the relative strength of specific cultural values within companies. Going a step further, the study also investigated which cultural values statistically impact financial performance. The results indicated that the organizational culture focusing on innovation and creativity (Innovation) and on customers and the market (Quality) positively influenced Tobin's Q, an indicator of a company's future value and growth. For the indicator of profitability, ROA, only the organizational culture emphasizing customers and the market (Quality) showed statistically significant impact. This study distinguishes itself from traditional surveys and case analysis-based research on organizational culture by analyzing large-scale text data to explore organizational culture.

• The Journal of Bigdata
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• v.4 no.2
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• pp.23-34
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• 2019

Recently, the crime that utilizes the digital platform is continuously increasing. About 140,000 cases occurred in 2015 and about 150,000 cases occurred in 2016. Therefore, it is considered that there is a limit handling those online crimes by old-fashioned investigation techniques. Investigators' manual online search and cognitive investigation methods those are broadly used today are not enough to proactively cope with rapid changing civil crimes. In addition, the characteristics of the content that is posted to unspecified users of social media makes investigations more difficult. This study suggests the site-based collection and the Open API among the content web collection methods considering the characteristics of the online media where the infringement crimes occur. Since illegal content is published and deleted quickly, and new words and alterations are generated quickly and variously, it is difficult to recognize them quickly by dictionary-based morphological analysis registered manually. In order to solve this problem, we propose a tokenizing method in the existing dictionary-based morphological analysis through WPM (Word Piece Model), which is a data preprocessing method for quick recognizing and responding to illegal contents posting online infringement crimes. In the analysis of data, the optimal precision is verified through the Vote-based ensemble method by utilizing a classification learning model based on supervised learning for the investigation of illegal contents. This study utilizes a sorting algorithm model centering on illegal multilevel business cases to proactively recognize crimes invading the public economy, and presents an empirical study to effectively deal with social data collection and content investigation.