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http://dx.doi.org/10.3743/KOSIM.2016.33.3.321

KONG-DB: Korean Novel Geo-name DB & Search and Visualization System Using Dictionary from the Web  

Park, Sung Hee (한남대학교 문헌정보학과)
Publication Information
Journal of the Korean Society for information Management / v.33, no.3, 2016 , pp. 321-343 More about this Journal
Abstract
This study aimed to design a semi-automatic web-based pilot system 1) to build a Korean novel geo-name, 2) to update the database using automatic geo-name extraction for a scalable database, and 3) to retrieve/visualize the usage of an old geo-name on the map. In particular, the problem of extracting novel geo-names, which are currently obsolete, is difficult to solve because obtaining a corpus used for training dataset is burden. To build a corpus for training data, an admin tool, HTML crawler and parser in Python, crawled geo-names and usages from a vocabulary dictionary for Korean New Novel enough to train a named entity tagger for extracting even novel geo-names not shown up in a training corpus. By means of a training corpus and an automatic extraction tool, the geo-name database was made scalable. In addition, the system can visualize the geo-name on the map. The work of study also designed, implemented the prototype and empirically verified the validity of the pilot system. Lastly, items to be improved have also been addressed.
Keywords
novel geo-name extraction; named entity recognition; Web knowledge base; conditional random fields; text mining; machine learning; novel geo-name map visualization;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 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.)
2 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)   DOI
3 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.)
4 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.)
5 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.)
6 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.)
7 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)   DOI
8 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.)
9 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.)
10 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)   DOI
11 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.)
12 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.
13 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.)
14 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)   DOI
15 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.
16 McCallum, A. K. (2002). MALLET: A machine learning for language toolkit. Retrieved from http://mallet.cs.umass.edu
17 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   DOI
18 Standford Named Entity Recognition. Retrieved from http://nlp.stanford.edu/software/CRF-NER.shtml
19 Python Beautiful Soup Library (2016. 8. 21). Retrieved from https://pypi.python.org/pypi/beautifulsoup4