Acknowledgement
Supported by : 한국연구재단
References
- 김성원, 나동렬 (2008). 2단계 최대 엔트로피 모델을 이용한 한국어 개체명 인식. 2008 한국정보과학회 학술 심포지움 논문집, 2(1), 81-86. (Kim, Seong-Won, & Ra, Dong-Yul (2008). Korean named entity recognition using two-level maximum entropy model. 2008 Annual Symposium Proceedings of Korean Institute of Information Science and Engineering, 2(1), 81-86.)
- 문상호 (2015). 엔그램뷰어를 이용한 인문학의 빅데이타 사례 연구. Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology, 5(6), 57-65. http://dx.doi.org/10.14257/AJMAHS.2015.12.10 (Moon, Sang-Ho (2015). Case study of big data in humanities using N-gram viewer. Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology, 5(6) December, 57-65 http://dx.doi.org/10.14257/AJMAHS.2015.12.10)
- 박철수 (2008). 문학지리학적 관점에서 본 북촌 도시한옥 밀집지역의 물리적 정체성에 관한 연구. 한국주거학회논문집, 19(2), 115-124. (Park, Cheol-Soo (2008). Physical identities of Bukchonhanok area viewed from literary geography. Journal of the Korean Housing Association, 19(2), 115-124.)
- 이은령 (2009). 19세기 문헌 국역본의 개체명 인식 및 관계 추출을 위한 기초 연구. 언어학, 53, 141-162. (Lee, Eunryoung (2009). Named entity detection and relation extraction in the personal chronology of the 19th century. Journal of the Linguistic Society of Korean, 53, 141-162.)
- 이은숙, 김일림, 정희선 (2007). 종로 문학공간의 데이터베이스 구축방안. 문화역사지리, 19, 1-14. (Lee, Eunsook, Kim, Il-Rim, & Cheong, Heesun (2007). An inquiry into the database construction of the literary space in Jongno area. Journal of Cultural and Historical Geography, 19, 1-14.)
- 이창기, 김준석, 김정희, 김현기 (2014). 딥 러닝을 이용한 개체명 인식. 2014 한국정보과학회 제41회 정기총회 및 동계학술발표회, 423-425. (Lee, Changki, Kim, Junseok, Kim, Jeonghee, & Kim, Hyunki (2014). Named entity recognition using deep learning. Proceedings of Korean Institute of Information Science and Engineering, 423-425.)
- 이창기, 장명길 (2010). Structural SVMs 및 Pegasos 알고리즘을 이용한 한국어 개체명 인식. 인지과학, 21(4), 655-667. http://dx.doi.org/10.19066/cogsci.2010.21.4.009 (Lee, Changki, & Jang, Myungil (2010). Named entity recognition with structural SVMs and pegasos algorithm. Korean Journal of Cognitive Science, 21(4), 655-667. http://dx.doi.org/10.19066/cogsci.2010.21.4.009)
- 장노현 (2008). 소설 속 지명정보 활용 방안 기초 연구. 한민족문화연구, 24, 255-283. (Jang, No Hyun (2008). A basic study on practical use of geographical designation in Korean novel. The Review of Korean Cultural Studies, 24, 255-283.)
- 장문현 (2015). 공간정보 기반의 감성문화지도 시각화 연구: 섬진강유역 역사문화유적을 대상으로. 국토지리학회지, 49(1), 27-39. (Jang, Mun Hyun (2015). A study on visualization of an emotional map based on spatial information: Focused on historical and cultural heritage in Seomjin river area. The Geographical Journal of Korea, 49(1), 27-39.)
- 최성필 (2016). 기계 학습을 이용한 바이오 분야 학술 문헌에서의 관계 추출에 대한 실험적 연구. 한국문헌정보학회지, 50(2), 309-336. http://dx.doi.org/10.4275/kslis.2016.50.2.309 (Choi, Sung-Pil (2016). An experimental study on the relation extraction from biomedical abstracts using machine learning. Journal of the Korean Society for Library and Information Science, 50(2), 309-336. http://dx.doi.org/10.4275/kslis.2016.50.2.309)
- 최진무, 김민준, 최돈곤 (2014). 지명 활용을 위한 지명 DB 와 수치지도 DB 의 연계 방안 연구. 대한지리학회지, 49(2), 310-319. (Choi, Jinmu, Kim, Min Jun & Choi, Don Gon (2014). Linking toponym database with digital map database. Journal of the Korean Geographical Society, 49(2), 310-319.)
- 한순미 (2013), 소설 속 지명과 감성지도: 지명 연구와 문학 연구의 접점을 기대하며, 지명학, 19, 151-188 (Han, Soon-mi (2013). A place name and emotional mapping shown in novels - Looking forward to a contact between a place-name study and literary research - Journal of the Place Name Society of Korea, 19, 151-188.)
- 황이규, 윤보현 (2003). 한국어 정보처리: HMM에 기반한 한국어 개체명 인식. 정보처리학회논문지 B, b10(2), 229-236. http://dx.doi.org/10.3745/kipstb.2003.10b.2.229 (Hwang, Yi-Gyu, & Yun, Bo-Hyun (2003). HMM-based Korean named entity recognition. The KIPS transactions. Part B, b10(2), 229-236. http://dx.doi.org/10.3745/kipstb.2003.10b.2.229)
- Finkel, J. R., Grenager, T., & Manning, C. (2005). Incorporating non-local information into information extraction systems by Gibbs sampling. Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005), 363-370.
- Lafferty, J., McCallum, A., & Pereira, F. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. Proceedings of the 18th International Conference on Machine Learning, 282-289.
- McCallum, A. K. (2002). MALLET: A machine learning for language toolkit. Retrieved from http://mallet.cs.umass.edu
- Park, S. H., Ehrich, R. W., & Fox, E. A. (2012). A hybrid two-stage approach for discipline-independent canonical representation extraction from references. Proceedings of the 12th ACM/IEEE-CS joint conference on digital libraries (JCDL '12). ACM, New York, NY, USA, 285-294. http://dx.doi.org/10.1145/2232817.2232871
- Python Beautiful Soup Library (2016. 8. 21). Retrieved from https://pypi.python.org/pypi/beautifulsoup4
- Standford Named Entity Recognition. Retrieved from http://nlp.stanford.edu/software/CRF-NER.shtml