A Muti-Resolution Approach to Restaurant Named Entity Recognition in KoreanWeb
- Authors
- Kang, Bo-Yeong; Kim, Dae-Won
- Issue Date
- Dec-2012
- Publisher
- 한국지능시스템학회
- Keywords
- Named entity classification; semantic feature; multi-resolution approach
- Citation
- International Journal of Fuzzy Logic and Intelligent systems, v.12, no.4, pp 277 - 284
- Pages
- 8
- Journal Title
- International Journal of Fuzzy Logic and Intelligent systems
- Volume
- 12
- Number
- 4
- Start Page
- 277
- End Page
- 284
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/26093
- ISSN
- 1598-2645
- Abstract
- Named entity recognition (NER) technique can play a crucial role in extracting information from the web. While NER systems with relatively high performances have been developed based on careful manipulation of terms with a statistical model, term mismatches often degrade the performance of such systems because the strings of all the candidate entities are not known a priori. Despite the importance of lexical-level term mismatches for NER systems, however, most NER approaches developed to date utilize only the term string itself and simple term-level features, and do not exploit the semantic features of terms which can handle the variations of terms effectively. As a solution to this problem, here we propose to match the semantic concepts of term units in restaurant named entities (NEs), where these units are automatically generated from multiple resolutions of a semantic tree. As a test experiment, we applied our restaurant NER scheme to 49,153 nouns in Korean restaurant web pages. Our scheme achieved an average accuracy of 87.89% when applied to test data, which was considerably better than the 78.70% accuracy obtained using the baseline system.
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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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