- Apply for Authority
- P-ISSN1013-0799
- E-ISSN2586-2073
- KCI
A Study on the Integration of Recognition Technology for Scientific Core Entities
- Downloaded
- Viewed
Abstract
Large-scaled information extraction plays an important role in advanced information retrieval as well as question answering and summarization. Information extraction can be defined as a process of converting unstructured documents into formalized, tabular information, which consists of named-entity recognition, terminology extraction, coreference resolution and relation extraction. Since all the elementary technologies have been studied independently so far, it is not trivial to integrate all the necessary processes of information extraction due to the diversity of their input/output formation approaches and operating environments. As a result, it is difficult to handle scientific documents to extract both named-entities and technical terms at once. In order to extract these entities automatically from scientific documents at once, we developed a framework for scientific core entity extraction which embraces all the pivotal language processors, named-entity recognizer and terminology extractor.
- keywords
- information extraction, named entity recognition, terminology extraction, information extraction, named entity recognition, terminology extraction, 정보추출, 개체명 인식, 전문용어 인식
Reference
국립국어원. (2007). 전문용어 연구:태학사.
김형철. (2009). CRF를 이용한 대명사 참조해서 시스템. 한글 및 한국어 정보처리 학술대회 발표논문집, 21(1), 87-91.
오종훈. (2006). 기계학습에 기반한 생의학분야 전문용어의 자동 인식. 정보과학회논문지 : 소프트웨어 및 응용, 33(8), 718-729.
이창기. (2006). Conditional Random Fields를 이용한 세부 분류 개체 명 인식. 한국정보과학회 언어공학연구회 학술발표논문집, 18, 268-272.
Ananiadou, S. Automatic terminology management in biomedicine.
Black, W. J. (2002). Language-independent named entity classification by modified transformation- based learning and by decision tree induction (159-162). Proceedings of CoNLL 2002.
Carreras, X. (2002). Named entity extraction using Ada- Boost (167-170). Proceedings of CoNLL 2002.
Chang, J. T. (2004). GAPSCORE: Finding gene and protein names one word at a time. Bioinformatics, 20(2), 216-225.
Collins, M. (2002). Ranking algorithms for named-entity extraction: boosting and the voted perceptron (-). Proceedings of ACL.
CoNLL(Conference on Computational Natural Language Learning). http://www.cnts.ua.ac.be/conll2002 /ner/.
CRF(Conditional Random Fields). http://en.wikipedia.org/wiki/Conditi onal_random_field.
DARTS(Double-ARray Trie System). http://chasen.org/~taku/software/ darts/.
Elsner, M. (2007). A Generative Discourse-New Model for Text Coherence. .
Extended Named Entity Hierarchy. http://nlp.cs.nyu.edu/ene/.
Grosz, B. J. (1995). Centering: A framework for modeling the local coherence of discourse. Computational Linguistics, 12(2), 203-225.
Lafferty, J.. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data (282-289). Proceedingsof International Conference on MachineLearning.
LDC. (2008). ACE(Automatic Content Extraction) English Annotation Guidelines for Entities, ver 6.6 (-). Linguistic Data Consortium.
Marcus, M. P. (2004). Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics, 19(2), 313-330.
Miller,G.A. (1995). WordNet: A lexical database for English. Communications of the ACM, 38(11), 39-41.
MUC(Message Understanding System). http://www.cs.nyu.edu/cs/faculty/ grishman/muc6.html.
Penn Treebank(The Penn Treebank Project). http://www.cis.upenn.edu/~treeba nk/.
Sasaki, Y.. (2008). BioLexicon: A lexical resource for the biology domain (-). Proceedings of the Third International Symposium on Semantic Mining in Biomedicine.
Sekine S. Personal Communications. http://nlp.cs.nyu.edu/oak/.
Tanabe, L. (2002). Tagging gene and protein names in biomedical text. Bioinformatics, 18(8), 1124-1132.
The Gene Ontology Consortium. (2008). The Gene ontology project in 2008. Nucleic Acids Research, 36, D440-D444.
Tjong Kim. (2002). Memory-based shallow parsing. Journal of Machine Learning Research, 2, 559-594.
Watanabe, Y.. (2007). A Graph-based approach to named entity categorization in Wikipedia using conditional random fields (649-657). Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning.
Weischedel, R.. (2007). OntoNotes Release 2.0:BBN Technologies.
Zhang, J. (2004). Enhancing HMM-based biomedical named entity recognition by studying special phenomena. Journal of Biomedical Informatics, 37(6), 411-422.
Zhou, G. (2004). Recognizing names in biomedical texts: A machine learning approach. Bioinformatics, 20(7), 1178-1190.
- Downloaded
- Viewed
- 0KCI Citations
- 0WOS Citations