Maximum Entropy-Based Named Entity Recognition Method for Multiple Social Networking Services
- Authors
- Jung, Jason J.
- Issue Date
- Nov-2012
- Publisher
- LIBRARY & INFORMATION CENTER, NAT DONG HWA UNIV
- Keywords
- Named entity recognition; Social network analysis; Multiplex social network; Contextual association; Microtexts
- Citation
- JOURNAL OF INTERNET TECHNOLOGY, v.13, no.6, pp 931 - 937
- Pages
- 7
- Journal Title
- JOURNAL OF INTERNET TECHNOLOGY
- Volume
- 13
- Number
- 6
- Start Page
- 931
- End Page
- 937
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/37725
- ISSN
- 1607-9264
2079-4029
- Abstract
- Given a certain question, named entity recognition (NER) methods are regarded as an efficient strategy to extract correct answers. The goal of this work is to extend such conventional NER methods for analyzing a set of microtexts of which lengths are relatively short. These microtexts are streaming through several different social networking services, e.g., Twitter and Face Book. To do so, we propose three heuristics for determining contextual associations between the microtexts, and discovering contextual clusters of microtexts, which can be expected to improve the performance of conventional NER tasks. Experimental results show the feasibility of the proposed mechanisms which extend the maximum entropy-based NER tasks for extracting relevant information in online social network applications.
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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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