@inproceedings{scholars656,
           pages = {599--602},
         address = {Kuala Lumpar},
           title = {A hybrid approach to semi-supervised named entity recognition in health, safety and environment reports},
         journal = {Proceedings - 2009 International Conference on Future Computer and Communication, ICFCC 2009},
             doi = {10.1109/ICFCC.2009.52},
            year = {2009},
            note = {cited By 5; Conference of 2009 International Conference on Future Computer and Communication, ICFCC 2009 ; Conference Date: 3 April 2009 Through 5 April 2009; Conference Code:78291},
          author = {Sari, Y. and Hassan, M. F. and Zamin, N.},
            isbn = {9780769535913},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-70449442869&doi=10.1109\%2fICFCC.2009.52&partnerID=40&md5=26f245e3f4f09232eeb8ab04b9b310e5},
        keywords = {Area of interest; Basilisk algorithm; Database entry; Health , safety and environments; Health and safety environment; Health safety; Hybrid approach; Link grammar; Main tasks; Monetary value; Name entity recognition; Named entity recognition; Natural language processing; Optimum solution; PETRONAS; Semi-supervised; Text mining, Algorithms; Computational linguistics; Health; Natural language processing systems, Character recognition},
        abstract = {In the last few years, text mining have become the area of interests in Natural Language Processing (NLP). They share a similar idea i.e. to extract important facts from unstructured text which later help to populate database entries. Name Entity Recognition (NER) is one of the main task needed to develop text mining systems in which it is used to identify and classify entities in the text into predefined categories such as the names of persons, organizations, locations, dates, times, quantities, monetary values, percentages, etc. This paper focuses on studying the optimum solution to perform NER. To achieve our target, Health Safety and Environment (HSE) reports available from the Universiti Teknologi PETRONAS (UTP) are chosen as the case study. The UTP's HSE reports are the investigation reports which contain the information on incidents and accidents occurred during the daily operations. Many algorithms have been reported for NER ranging from simple statistical methods to advanced Natural language Processing (NLP) methods. This paper describes the possibility to apply Link Grammar (LG) and Basilisk Algorithm in NER. {\^A}{\copyright} 2009 IEEE.}
}