• Knowledge Management Research
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• v.21 no.1
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• pp.117-136
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• 2020

In the studies for the recommender systems which solve the information overload problem of users, the use of transactional data has been continuously tried. Especially, because the firms can easily obtain transactional data along with the development of IoT technologies, transaction-based recommender systems are recently used in various areas. However, the use of transactional data has limitations that it is hard to reflect domain knowledge and they do not directly show user preferences for individual items. Therefore, in this study, we propose a method applying the word embedding in the transaction-based recommender system to reflect preference differences among users and domain knowledge. Our approach is based on SAR, which shows high performance in the recommender systems, and we improved its components by using FastText, one of the word embedding techniques. Experimental results show that the reflection of domain knowledge and preference difference has a significant effect on the performance of recommender systems. Therefore, we expect our study to contribute to the improvement of the transaction-based recommender systems and to suggest the expansion of data used in the recommender system.

• Journal of KIISE
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• v.44 no.3
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• pp.306-313
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• 2017

Named entity recognition (NER) seeks to locate and classify named entities in text into pre-defined categories such as names of persons, organizations, locations, expressions of times, etc. Recently, many state-of-the-art NER systems have been implemented with bidirectional LSTM CRFs. Deep learning models based on long short-term memory (LSTM) generally depend on word representations as input. In this paper, we propose an approach to expand word representation by using pre-trained word embedding, part of speech (POS) tag embedding, syllable embedding and named entity dictionary feature vectors. Our experiments show that the proposed approach creates useful word representations as an input of bidirectional LSTM CRFs. Our final presentation shows its efficacy to be 8.05%p higher than baseline NERs with only the pre-trained word embedding vector.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.394-395
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• 2016

워드 임베딩(word embedding)은 정보검색이나 기계학습에서 단어를 표현하기 위하여 사용되던 기존의 one-hot 벡터 방식의 희소공간 및 단어들 간의 관계정보를 유지할 수 없는 문제를 해결하기 위한 방법이다. 워드 임베딩의 한 방법으로 word2vec은 최근 빠른 학습시간과 높은 효과를 얻을 수 있는 모델로 주목을 받고 있다. word2vec은 수행 시 주어지는 옵션인 벡터차원과 문맥크기에 의해 그 결과 품질이 상이하다. Mikolov는 구글 뉴스 문헌 집합에 대하여 word2vec을 실험하고, 적합한 옵션을 제시하였다. 본 논문에서는 구글 뉴스 문헌 같은 일반 문서가 아닌 생의학 분야에 특화된 문헌에 대하여 word2vec에 대한 다양한 옵션을 실험하고, 생의학 문헌에 적합한 최적의 조건을 분석한다.

• Proceedings of The KACE
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• 2017.08a
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• pp.157-160
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• 2017

최근 자연 언어 처리 분야에서는 단어를 실수벡터로 임베딩하는 워드 임베딩(Word embedding) 기술이 많은 각광을 받고 있다. 최근에는 서로 다른 두 언어를 이용한 이중 언어 위드 임베딩(Bilingual word embedding) 방법을 사용하는 연구가 많이 이루어지고 있는데, 이중 언어 워드 임베딩에서 임베딩 절과의 질은 학습하는 코퍼스의 정렬방식에 따라 많은 영향을 받는다. 본 논문은 자막 병렬 코퍼스를 이용하여 밑바탕 어휘집(Seed lexicon)을 구축하여 번역 연결 강도를 향상시키고, 이중 언어 워드 임베딩의 사천(Vocabulary) 확장을 위한 언어별 연결 함수(Language-specific mapping function)을 학습하는 새로운 방식의 모델을 제안한다. 제안한 모델은 기존 모델과의 성능비교에서 비교할만한 수준의 결과를 얻었다.

• Journal of the Korea Society of Computer and Information
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• v.21 no.7
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• pp.9-16
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• 2016

Collaborative filtering(CF) uses the purchase or item rating history of other users, but does not need additional properties or attributes of users and items. Hence CF is known th be the most successful recommendation technology. But conventional CF approach has some significant weakness, such as the new user problem. In this paper, we propose a approach using word embedding with skip-gram for learning distributed item representations. In particular, we show that this approach can be used to capture precise item for solving the "new user problem." The proposed approach has been tested on the Movielens databases. We compare the performance of the user based CF, item based CF and our approach by observing the change of recommendation results according to the different number of item rating information. The experimental results shows the improvement in our approach in measuring the precision applied to new user problem situations.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.11-12
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• 2023

본 논문에서는 상점 내 캡티브 포털을 활용하여 수집된 주문 정보 데이터를 바탕으로 사용자가 선호하는 메뉴를 추천하는 시스템을 제안한다. 이 시스템은 식품 관련 공공 데이터셋으로 학습된 단어 임베딩 모델(Word Embedding Model)로 메뉴명을 벡터화하여 그와 유사한 벡터를 가지는 메뉴를 추천한다. 이 기법은 캡티브 포털에서 수집되는 데이터 특성상 사용자의 개인정보가 비식별화 되고 선택 항목에 대한 정보도 제한되므로 기존의 단어 임베딩 모델을 추천 시스템에 적용하는 경우에 비해 유리하다. 본 논문에서는 실제 동일한 시스템을 사용하는 상점들의 구매 기록 데이터를 활용한 검증 데이터를 확보하여 제안된 추천 시스템이 Precision@k(k=3) 구매 예측에 유의미함을 보인다.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.173-178
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• 2021

In this paper, we find a suitable methodology for Korean Sentiment Analysis through a comparative experiment in which methods of embedding and natural network models are learned at the highest accuracy and fastest speed. The embedding method compares word embeddeding and Word2Vec. The model compares and experiments representative neural network models CNN, RNN, LSTM, GRU, Bi-LSTM and Bi-GRU with IKEA review data. Experiments show that Word2Vec and BiGRU had the highest accuracy and second fastest speed with 94.23% accuracy and 42.30 seconds speed. Word2Vec and GRU were found to have the third highest accuracy and fastest speed with 92.53% accuracy and 26.75 seconds speed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.25-30
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• 2018

Recently with the development of Internet technology, a lot of research area such as retrieval and extracting data have getting important for providing the information efficiently and quickly. Especially, the technique of analyzing and finding the semantic similar words for given korean word such as compound words or generated newly is necessary because it is not easy to catch the meaning or semantic about them. To handle of this problem, word clustering is one of the technique which is grouping the similar words of given word. In this paper, we proposed the korean language clustering technique that clusters the similar words by embedding the words using Word2Vec from the given documents.

• KIISE Transactions on Computing Practices
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• v.22 no.7
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• pp.315-319
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• 2016

Recurrent neural network based language models (RNN LM) have shown improved results in language model researches. The RNN LMs are limited to post processing sessions, such as the N-best rescoring step of the wFST based speech recognition. However, it has considerable vocabulary problems that require large computing powers for the LM training. In this paper, we try to find the 1st pass N-gram model using word embedding, which is the simplified deep neural network. The class based language model (LM) can be a way to approach to this issue. We have built class based vocabulary through word embedding, by combining the class LM with word N-gram LM to evaluate the performance of LMs. In addition, we propose that part-of-speech (POS) tagging based LM shows an improvement of perplexity in all types of the LM tests.