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최선화(SeonHwa Choi),박혁로(Hyukro Park) 한국정보과학회 2003 한국정보과학회 학술발표논문집 Vol.30 No.1B
본 논문에서는 코퍼스를 이용한 확률 의존문법 자동 생성 기술을 다룬다. 의존문법 생성을 위해 구성성분의 기능어들 간의 의존관계를 학습했던 기존 연구와는 달리, 한국어 구성성분은 내용어와 기능어의 결합 형태로 구성되고 임의 구성성분 기능어와 임의 구성성분 내용어간의 의존관계가 의미가 있다는 사실을 반영한 의존문법 학습방법을 제안한다. KAIST의 트리 부착 코퍼스 31,086 문장에서 추출한 30,600문장의 Tagged Corpus을 가지고 학습한 결과 초기문법을 64%까지 줄인 1,101개의 의존문법을 획득했고, 실험문장 486문장을 Parsing한 결과 73.81%의 Parsing 정확도를 보였다.
Mobility Prediction Algorithms Using User Traces in Wireless Networks
Luong, Chuyen,Do, Son,Park, Hyukro,Choi, Deokjai Korea Multimedia Society 2014 멀티미디어학회논문지 Vol.17 No.8
Mobility prediction is one of hot topics using location history information. It is useful for not only user-level applications such as people finder and recommendation sharing service but also for system-level applications such as hand-off management, resource allocation, and quality of service of wireless services. Most of current prediction techniques often use a set of significant locations without taking into account possible location information changes for prediction. Markov-based, LZ-based and Prediction by Pattern Matching techniques consider interesting locations to enhance the prediction accuracy, but they do not consider interesting location changes. In our paper, we propose an algorithm which integrates the changing or emerging new location information. This approach is based on Active LeZi algorithm, but both of new location and all possible location contexts will be updated in the tree with the fixed depth. Furthermore, the tree will also be updated even when there is no new location detected but the expected route is changed. We find that our algorithm is adaptive to predict next location. We evaluate our proposed system on a part of Dartmouth dataset consisting of 1026 users. An accuracy rate of more than 84% is achieved.