• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.167-172
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• 2021

관용표현 중에는 중의성을 가진 표현이 많다. 즉 하나의 표현이 맥락에 따라 일반적 의미와 관용적 의미 두 가지 이상으로 해석될 가능성이 있어 이런 유형의 관용표현을 중의성 해소 없이 자연어 처리 태스크에 적용할 경우 문제가 발생하게 된다. 본 연구에서는 관용표현의 특성인 중의성과 더불어 '관용표현은 이미 사용자의 머릿속에 하나의 토큰으로 저장되어 있다'라는 'Idiom Principle'을 바탕으로 관용표현에 대해 각각 표면형, 단순 단일 토큰형, stemming 단일 토큰형 층위의 임베딩을 만들어 관용표현 분류 연구를 진행하였으며, 실험 결과 표면형 및 stemming을 적용하지 않은 단순 단일 토큰으로 학습하는 것보다, stemming을 적용한 후 단일 토큰으로 학습하는 것이 관용표현의 중의성 해소에 유의미한 효과가 있음을 확인하였다.

• Journal of the Korea Convergence Society
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• v.10 no.6
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• pp.41-47
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• 2019

It is very important to search for and obtain an example of a similar judgment in case of court judgment. The existing judge's document search uses a method of searching through key-words entered by the user. However, if it is necessary to input an accurate keyword and the keyword is unknown, it is impossible to search for the necessary document. In addition, the detected document may have different contents. In this paper, we want to improve the effectiveness of the method of vectorizing a document into a three-dimensional space, calculating cosine similarity, and searching close documents in order to search an accurate judge's example. Therefore, after analyzing the similarity of words used in the judge's example, a method is provided for extracting the mode and inserting it into the text of the text, thereby providing a method for improving the cosine similarity of the document to be retrieved. It is hoped that users will be able to provide a fast, accurate search trying to find an example of a tax-related judge through the proposed model.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.145-165
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• 2023

Recommendation system plays a significant role on relieving difficulties of selecting information among rapidly increasing amount of information caused by the development of the Internet and on efficiently displaying information that fits individual personal interest. In particular, without the help of recommendation system, E-commerce and OTT companies cannot overcome the long-tail phenomenon, a phenomenon in which only popular products are consumed, as the number of products and contents are rapidly increasing. Therefore, the research on recommendation systems is being actively conducted to overcome the phenomenon and to provide information or contents that are aligned with users' individual interests, in order to induce customers to consume various products or contents. Usually, collaborative filtering which utilizes users' historical behavioral data shows better performance than contents-based filtering which utilizes users' preferred contents. However, collaborative filtering can suffer from cold-start problem which occurs when there is lack of users' historical behavioral data. In this paper, hybrid music recommendation system, which can solve cold-start problem, is proposed based on the playlist data of Melon music streaming service that is given by Kakao Arena for music playlist continuation competition. The goal of this research is to use music tracks, that are included in the playlists, and metadata of music tracks and playlists in order to predict other music tracks when the half or whole of the tracks are masked. Therefore, two different recommendation procedures were conducted depending on the two different situations. When music tracks are included in the playlist, LightFM is used in order to utilize the music track list of the playlists and metadata of each music tracks. Then, the result of Item2Vec model, which uses vector embeddings of music tracks, tags and titles for recommendation, is combined with the result of LightFM model to create final recommendation list. When there are no music tracks available in the playlists but only playlists' tags and titles are available, recommendation was made by finding similar playlists based on playlists vectors which was made by the aggregation of FastText pre-trained embedding vectors of tags and titles of each playlists. As a result, not only cold-start problem can be resolved, but also achieved better performance than ALS, BPR and Item2Vec by using the metadata of both music tracks and playlists. In addition, it was found that the LightFM model, which uses only artist information as an item feature, shows the best performance compared to other LightFM models which use other item features of music tracks.