딥러닝 기법을 이용한 철도 운행 관련 비정형 SNS 메시지 정형화
Structuring of Unstructured SNS Messages on Rail Services using Deep Learning Techniques
1
조회
0
다운로드
2
인용
0
참고문헌
키워드
초록
This paper presents a structuring process of unstructured social network service (SNS) messages on rail services. We crawl messages about rail services posted on SNS and extract keywords indicating date and time, rail operating company, station name, direction, and rail service types from each message. Among them, the rail service types are classified by machine learning according to predefined rail service types, and the rest are extracted by regular expressions. Words are converted into vector representations using Word2Vec and a conventional Convolutional Neural Network (CNN) is used for training and classification. For performance measurement, our experimental results show a comparison with a TF-IDF and Support Vector Machine (SVM) approach. This structured information in the database and can be easily used for services for railway users.
Abstract
This paper presents a structuring process of unstructured social network service (SNS) messages on rail services. We crawl messages about rail services posted on SNS and extract keywords indicating date and time, rail operating company, station name, direction, and rail service types from each message. Among them, the rail service types are classified by machine learning according to predefined rail service types, and the rest are extracted by regular expressions. Words are converted into vector representations using Word2Vec and a conventional Convolutional Neural Network (CNN) is used for training and classification. For performance measurement, our experimental results show a comparison with a TF-IDF and Support Vector Machine (SVM) approach. This structured information in the database and can be easily used for services for railway users.
참고문헌 (13)
- "K. indicator"[URL]
- "Rail Safety Information System"[URL]
- 김남규, "텍스트 분석 기술 및 활용 동향", 한국통신학회논문지, 42(2), pp.471~492 (2017), 한국통신학회[DOI]
- Gerard Salton, "Term-weighting approaches in automatic text retrieval", Information Processing & Management, 24(5), pp.513~523 (1988), Elsevier BV[DOI]
- 김도우, "Doc2Vec과 Word2Vec을 활용한 Convolutional Neural Network 기반 한국어 신문 기사 분류", 정보과학회논문지, 44(7), pp.742~747 (2017), 한국정보과학회
- Y. LeCun, "The handbook of brain theory and neural networks", pp.255~258 (1998)
- Y. Kim, "Convolutional Neural Networks for Sentence Classification", Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp.1746~1751 (2014)
- T. Mikolov, "Efficient Estimation of Word Representations in Vector Space", arXiv:1301.3781v3, ~ (2013)
- Q. Le., "Distributed Representation of Sentences and Documents", Proceedings of the 31st International Conference on International Conference on Machine Learning, 32(2), pp.1188~1196 (2014)
- Y. Bengio, "A Neural Probabilistic Language Model", Journal of Machine Learning Research, 3(6), pp.1137~1155 (2003)
- "Wiki Web Site"[URL]
- 김우주, "Word2vec을 활용한 문서의 의미 확장 검색방법", 한국콘텐츠학회 논문지, 16(10), pp.687~692 (2016), 한국콘텐츠학회[DOI]