A maximum entropy approach to natural language processing
Computational Linguistics
CORDER: COmmunity relation discovery by named entity recognition
Proceedings of the 3rd international conference on Knowledge capture
Named entity recognition with a maximum entropy approach
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
ANERsys: An Arabic Named Entity Recognition System Based on Maximum Entropy
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Brief Communication: Two-phase biomedical named entity recognition using CRFs
Computational Biology and Chemistry
Reusing ontology mappings for query routing in semantic peer-to-peer environment
Information Sciences: an International Journal
Short text classification in twitter to improve information filtering
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
A composite kernel for named entity recognition
Pattern Recognition Letters
Service chain-based business alliance formation in service-oriented architecture
Expert Systems with Applications: An International Journal
SyMSS: A syntax-based measure for short-text semantic similarity
Data & Knowledge Engineering
Evolutionary approach for semantic-based query sampling in large-scale information sources
Information Sciences: an International Journal
A term normalization method for efficient knowledge acquisition through text processing
Multimedia Tools and Applications
Emotion-based character clustering for managing story-based contents: a cinemetric analysis
Multimedia Tools and Applications
FS-NER: a lightweight filter-stream approach to named entity recognition on twitter data
Proceedings of the 22nd international conference on World Wide Web companion
| Hi-index | 12.05 |
Named entity recognition (NER) methods have been regarded as an efficient strategy to extract relevant entities for answering a given query. The aim of this work is to exploit the conventional NER methods for analyzing a large set of microtexts of which lengths are short. Particularly, the microtexts are streaming on online social media, e.g., Twitter. To do so, this paper proposes three properties of contextual association among the microtexts to discover contextual clusters of the microtexts, which can be expected to improve the performance of NER tasks. As a case study, we have applied the proposed NER system to Twitter. Experimental results demonstrate the feasibility of the proposed method (around 90.3% of precision) for extracting relevant information in online social network applications.