• Proceedings of the KAIS Fall Conference
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• 2004.11a
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• pp.141-144
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• 2004

질의응답 시스템이 정보검색 시스템과 다른 중요한 점은 질의 처리 과정이며, 자연어 질의 문장에서 사용자의 질의 의도를 파악하여 질의 유형을 분류하는 것이다. 본 논문에서는 질의 주-형을 분류하기 위해 복잡한 분류 규칙이나 대용량의 사전 정보를 이용하지 않고 질의 문장에서 의문사에 해당하는 어휘들을 추출하고 주변에 나타나는 명사들의 의미 정보를 이용하여 세부적인 정답 유형을 결정할 수 있는 질의 유형 분류 방법을 제안한다. 의문사가 생략된 경우의 처리 방법과 동의어 정보와 접미사 정보를 이용하여 질의 유형 분류 성능을 향상시킬 수 있는 방법을 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1778-1799
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• 2022

Automatic text summarization is a procedure that packs enormous content into a more limited book that incorporates significant data. Malayalam is one of the toughest languages utilized in certain areas of India, most normally in Kerala and in Lakshadweep. Natural language processing in the Malayalam language is relatively low due to the complexity of the language as well as the scarcity of available resources. In this paper, a way is proposed to deal with the text summarization process in Malayalam documents by training a model based on the Support Vector Machine classification algorithm. Different features of the text are taken into account for training the machine so that the system can output the most important data from the input text. The classifier can classify the most important, important, average, and least significant sentences into separate classes and based on this, the machine will be able to create a summary of the input document. The user can select a compression ratio so that the system will output that much fraction of the summary. The model performance is measured by using different genres of Malayalam documents as well as documents from the same domain. The model is evaluated by considering content evaluation measures precision, recall, F score, and relative utility. Obtained precision and recall value shows that the model is trustable and found to be more relevant compared to the other summarizers.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.39-47
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• 2021

The construction of high-quality input features through effective segmentation is essential for increasing the sentence comprehension of a language model. Improving the quality of them directly affects the performance of the downstream task. This paper comparatively studies the segmentation that effectively reflects the linguistic characteristics of Korean regarding word and sentence classification. The segmentation types are defined in four categories: eojeol, morpheme, syllable and subchar, and pre-training is carried out using the RoBERTa model structure. By dividing tasks into a sentence group and a word group, we analyze the tendency within a group and the difference between the groups. By the model with subchar-level segmentation showing higher performance than other strategies by maximal NSMC: +0.62%, KorNLI: +2.38%, KorSTS: +2.41% in sentence classification, and the model with syllable-level showing higher performance at maximum NER: +0.7%, SRL: +0.61% in word classification, the experimental results confirm the effectiveness of those schemes.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.115-119
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• 2023

The natural language processing models used in current artificial intelligence are huge, causing various difficulties in processing and analyzing data in real time. In order to solve these difficulties, we proposed a method to improve the efficiency of processing by using less memory and checked the performance of the proposed model. The technique applied in this paper to evaluate the performance of the proposed model is to divide the large corpus by adjusting the number of attention heads and embedding size of the BERT[1] model to be small, and the results are calculated by averaging the output values of each forward. In this process, a random offset was assigned to the sentences at every epoch to provide diversity in the input data. The model was then fine-tuned for classification. We found that the split processing model was about 12% less accurate than the unsplit model, but the number of parameters in the model was reduced by 56%.

• Speech Sciences
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• v.12 no.2
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• pp.183-191
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• 2005

This paper describes the automated call routing for call center system based on speech recognition. We focus on the task of automatically routing telephone calls based on a users fluently spoken response instead of touch tone menus in an interactive voice response system. Vector based call routing algorithm is investigated and normalization method suggested. Call center database which was collected by KT is used for call routing experiment. Experimental results evaluating call-classification from transcribed speech are reported for that database. In case of small training data, an average call routing error reduction rate of 9% is observed when normalization method is used.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.855-865
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• 1996

In this paper, we propose a automation of the classification process for reusable software component and construction method of structured software components library. In order to efficient and automatic classification of software component, we decide the facets to represent characteristics of software component by acquiring semantic and syntactic information from software components descriptions in natural language, and compose the software component identifier or automatic extract terms corresponds to each facets. And then, in order to construct the structured software components library, we sore in the near location with software components of similar characteristic according to semantic similarity of the classified software components. As the result of applying proposed method, we can easily identify similar software components, the classification process of software components become simple, and the software components store in the structured software components library.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1391-1399
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• 2015

In recent times, studies sentiment analysis are being actively conducted by implementing natural language processing technologies for analyzing subjective data such as opinions and attitudes of users expressed on the Web, blogs, and social networking services (SNSs). Conventionally, to classify the sentiments in texts, most studies determine positive/negative/neutral sentiments by assigning polarity values for sentiment vocabulary using sentiment lexicons. However, in this study, sentiments are classified based on Thayer's model, which is psychologically defined, unlike the polarity classification used in opinion mining. In this paper, as a method for classifying the sentiments, sentiment categories are proposed by extracting sentiment keywords for major sentiments by using hashtags, which are essential elements of Instagram. By applying sentiment categories to user posts, sentiments can be determined through the similarity measurement between the sentiment adjective candidates and the sentiment keywords. The test results of the proposed method show that the average accuracy rate for all the sentiment categories was 90.7%, which indicates good performance. If a sentiment classification system with a large capacity is prepared using the proposed method, then it is expected that sentiment analysis in various fields will be possible, such as for determining social phenomena through SNS.

• Journal of Information Management
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• v.26 no.2
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• pp.61-79
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• 1995

The purpose of this paper is to investigate search efficiency of controlled vocabulary versus uncontrolled vocabulary subject access in online catalog systems. The question of the effectiveness of controlled versus uncontrolled vocabulary in information retrieval has been raised in many literatures. A debate continues in the Library and Information Science Professions over the relative merit, appropriateness, and efficiency of uncontrolled vocabulary subject access in online catalog systems. Actually users used to combine uncontrolled vocabulary subject searching with controlled vocabulary subject searching. But the success of user's subject search depends on his choice of search terms. Also the technical developments that facilitate cooperation among information services in general make it increasingly possible for such cooperation to take place on an international level. In this study, several common types of vocabularies on online catalog systems are described and compared, especially usages of vocabularies in multilingual environment are analyzed.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.223-229
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• 2022

LinkedIn is one of the most job hunting and career-growing applications in the world. There are a lot of opportunities and jobs available on LinkedIn. According to statistics, LinkedIn has 738M+ members. 14M+ open jobs on LinkedIn and 55M+ Companies listed on this mega-connected application. A lot of vacancies are available daily. LinkedIn data has been used for the research work carried out in this paper. This in turn can significantly tackle the challenges faced by LinkedIn and other job posting applications to improve the levels of jobs available in the industry. This research introduces Text Processing in natural language processing on datasets of LinkedIn which aims to find out the jobs that appear most in a month or/and year. Therefore, the large data became renewed into the required or needful source. This study thus uses Multinomial Naïve Bayes and Linear Support Vector Machine learning algorithms for text classification and developed a trained multilingual dataset. The results indicate the most needed job vacancies in any field. This will help students, job seekers, and entrepreneurs with their career decisions

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.1090-1100
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• 2024

While the incorporating ESG indicator is recognized as crucial for sustainability and increased firm value, inconsistent disclosure of ESG data and vague assessment standards have been key challenges. To address these issues, this study proposes an ambiguous text-based automated ESG rating strategy. Earnings Call Transcript data were classified as E, S, or G using the Refinitiv-Sustainable Leadership Monitor's over 450 metrics. The study employed advanced natural language processing techniques such as BERT, RoBERTa, ALBERT, FinBERT, and ELECTRA models to precisely classify ESG documents. In addition, the authors computed the average predicted probabilities for each label, providing a means to identify the relative significance of different ESG factors. The results of experiments demonstrated the capability of the proposed methodology in enhancing ESG assessment criteria established by various rating agencies and highlighted that companies primarily focus on governance factors. In other words, companies were making efforts to strengthen their governance framework. In conclusion, this framework enables sustainable and responsible business by providing insight into the ESG information contained in Earnings Call Transcript data.