• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.627-630
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• 2022

Over the past few years, fake news has become one of the most significant problems. Since it is impossible to prevent people from spreading misinformation, people should analyze the news themselves. However, this process takes some time and effort, so the routine part of this analysis should be automated. There are many different approaches to this problem, but they only analyze the text and messages, ignoring the images. The fake news problem should be solved using a complex analysis tool to reach better performance. In this paper, we propose the approach of training an Artificial Intelligence using an unsupervised learning algorithm, combined with online data parsing tools, providing independence from subjective data set. Therefore it will be more difficult to spread fake news since people could quickly check if the news or article is trustworthy.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.731-733
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• 2022

본 서비스는 문서 내의 가중치를 분석하여 키워드와 관련된 순서대로 정렬하여 판례/법률 검색의 정확도를 향상할 것을 제안한다. 상용화된 다른 판례/법률 관련 서비스의 경우, 키워드 검색을 통해 자신의 사례를 검색할 때, 요약된 정보가 없거나 너무 짧아 사용자가 원하는 판례/법률 결과를 얻을 수가 없어 본 서비스를 기획하게 되었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.404-406
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• 2023

본 논문은 맞춤형 카페 추천 서비스를 제안한다. 대중적인 포털 사이트의 카페 정보와 사용자 리뷰를 크롤링 하여 지역별, 키워드별 카페 데이터를 수집한다. 사용자가 원하는 지역과 임의의 키워드를 기준으로 데이터셋 내의 키워드와 비교하여 가장 유사한 키워드를 추출한다. spaCy 라이브러리의사전 학습된 모델 중 similarity method를 사용하여 추출된 키워드를 바탕으로 해당하는 카페를 추천한다. 이를 통해 사용자는 불필요한 정보를 걸러내고 쉽게 원하는 정보를 얻을 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.624-626
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• 2023

Credit scoring is a technique used by financial institutions to assess the creditworthiness of potential borrowers. This involves evaluating a borrower's credit history to predict the likelihood of defaulting on a loan. This paper presents an ensemble of two Transformer based models within a framework for discriminating the default risk of loan applications in the field of credit scoring. The first model is FinBERT, a pretrained NLP model to analyze sentiment of financial text. The second model is FT-Transformer, a simple adaptation of the Transformer architecture for the tabular domain. Both models are trained on the same underlying data set, with the only difference being the representation of the data. This multi-modal approach allows us to leverage the unique capabilities of each model and potentially uncover insights that may not be apparent when using a single model alone. We compare our model with two famous ensemble-based models, Random Forest and Extreme Gradient Boosting.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.636-638
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• 2023

Covid19의 세계적 유행 이래로 긴 일정의 해외여행이 감소하고 국내 여행의 수요가 꾸준히 증가하는 추세이다. 현재 다수의 국내 여행 숙박 플랫폼은 가성비 측면으로 이용자가 숙박업소를 선택하고 소비자와 업체를 연결해주는 과정에서 수수료를 얻는 상업적 모델이다. 본 논문에서는 가격 경쟁 중심의 기성 시스템이 아닌, 여행자 개인의 가치를 맞춤화하고 공익의 목적으로 업체를 홍보하는 시스템을 제안한다. 이 시스템은 웹 기반의 시스템을 구현하여 여행자에게 개인 가치에 맞는 업소를 맞춤형으로 추천하고 해당 업소에 대한 평가 지표를 시각화하여 제공한다. 본 시스템은 맞춤형 업소 추천과 평가 지표 제공을 위해 소비자의 리뷰 데이터를 사용한다. 텍스트 데이터를 분석하고 해당 데이터를 다중 분류를 통해 업소에 대한 평가 지표별 점수를 산정한다. 본 시스템은 여행자에게 다양한 관광지와 관광 업소를 추천함으로써 지역 관광을 유도하고 해당 여행지 업소와 지역 경제에 도움을 줄 것이라고 기대된다. 본 논문에서 제안된 기법은 오픈소스로 공개되었다[1].

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.501-502
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• 2023

본 논문은 자연어 처리(Natural Language Processing, NLP) 기술과 Flutter 언어를 활용하여 E-스포츠(E-Sports) 애플리케이션을 개발하는 방법을 제안한다. E-스포츠는 전 세계적으로 급속히 성장하는 산업이며, 많은 팬과 선수들이 참여하고 있다. 그러나 E-스포츠 관련 정보를 찾고 이해하기 위해서는 다양한 데이터를 직접 검색하고 분석해야 하는 어려움이 있다. 이러한 어려움을 극복하기 위해 자연어 처리 기술을 활용한 챗봇이 접목된 E-스포츠 애플리케이션을 개발하여 사용자가 효율적으로 관련 정보를 얻을 수 있도록 한다.

• Journal of Electrical Engineering and information Science
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• v.1 no.1
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• pp.1-9
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• 1996

A key issue in spoken language processing has become the integration of speech understanding and natural language processing(NLP). This paper presents a parallel computational model for the integration of speech and NLP. The model adopts a hierarchically-structured knowledge base and memory-based parsing techniques. Processing is carried out by passing multiple markers in parallel through the knowledge base. Speech-specific problems such as insertion, deletion, and substitution have been analyzed and their parallel solutions are provided. The complete system has been implemented on the Semantic Network Array Processor(SNAP) and is operational. Results show an 80% sentence recognition rate for the Air Traffic Control domain. Moreover, a 15-fold speed-up can be obtained over an identical sequential implementation with an increasing speed advantage as the size of the knowledge base grows.

• 대한상한금궤의학회지
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• v.14 no.1
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• pp.41-50
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• 2022

Objective : The purpose of this study is to explore the most suitable machine learning model algorithm for Shanghanlun diagnostic system classification using natural language processing (NLP). Methods : A total of 201 data items were collected from 『Shanghanlun』 and 『Clinical Shanghanlun』, 'Taeyangbyeong-gyeolhyung' and 'Eumyangyeokchahunobokbyeong' were excluded to prevent oversampling or undersampling. Data were pretreated using a twitter Korean tokenizer and trained by logistic regression, ridge regression, lasso regression, naive bayes classifier, decision tree, and random forest algorithms. The accuracy of the models were compared. Results : As a result of machine learning, ridge regression and naive Bayes classifier showed an accuracy of 0.843, logistic regression and random forest showed an accuracy of 0.804, and decision tree showed an accuracy of 0.745, while lasso regression showed an accuracy of 0.608. Conclusions : Ridge regression and naive Bayes classifier are suitable NLP machine learning models for the Shanghanlun diagnostic system classification.

• Science of Emotion and Sensibility
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• v.15 no.4
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• pp.425-440
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• 2012

As the scale of the internet grows, the amount of subjective data increases. Thus, A need to classify automatically subjective data arises. Sentiment classification is a classification of subjective data by various types of sentiments. The sentiment classification researches have been studied focused on NLP(Natural Language Processing) and sentiment word dictionary. The former sentiment classification researches have two critical problems. First, the performance of morpheme analysis in NLP have fallen short of expectations. Second, it is not easy to choose sentiment words and determine how much a word has a sentiment. To solve these problems, this paper suggests a combination of using web-scale data and a statistical approach to sentiment classification. The proposed method of this paper is using statistics of words from web-scale data, rather than finding a meaning of a word. This approach differs from the former researches depended on NLP algorithms, it focuses on data. Hadoop and MapReduce will be used to handle web-scale data.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.15-23
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• 2022

In the field of patents, as NLP(Natural Language Processing) is a challenging task due to the linguistic specificity of patent literature, there is an urgent need to research a language model optimized for Korean patent literature. Recently, in the field of NLP, there have been continuous attempts to establish a pre-trained language model for specific domains to improve performance in various tasks of related fields. Among them, ELECTRA is a pre-trained language model by Google using a new method called RTD(Replaced Token Detection), after BERT, for increasing training efficiency. The purpose of this paper is to propose KorPatELECTRA pre-trained on a large amount of Korean patent literature data. In addition, optimal pre-training was conducted by preprocessing the training corpus according to the characteristics of the patent literature and applying patent vocabulary and tokenizer. In order to confirm the performance, KorPatELECTRA was tested for NER(Named Entity Recognition), MRC(Machine Reading Comprehension), and patent classification tasks using actual patent data, and the most excellent performance was verified in all the three tasks compared to comparative general-purpose language models.