A New Fuzzy Support Vector Machine Method for Named Entity Recognition
Alireza Mansouri, Lilly Suriani Affendy, Ali Mamat · 2008
Recognizing and extracting exact name entities, like Persons, Locations, Organizations, Dates and Times are very useful to mining information from electronics resources and text. Learning to extract these types of data is called Named Entity Recognition (NER) task. Proper named entity recognition and extraction is important to solve most problems in hot research area such as Question Answering and Summarization Systems, Information Retrieval and Information Extraction, Machine Translation, Video Annotation, Semantic Web Search and Bioinformatics. In this paper we have improved the precision in NER from text using the new proposed method that calls FSVM. In our method we have employed Support Vector Machine as one of the best machine learning algorithm for classification and contribute a new fuzzy membership function thus removing the Support Vector Machine’s weakness points in NER precision and multi classification. The design of our method is a kind of One-Against-All multi classification technique to solve the traditional binary classifier in SVM.