Named Entity Recognition using Machine Learning Methods and Pattern-Selection Rules

Named Entity recognition, as a task of providing important semantic information, is a critical first step in Information Extraction and QuestionAnswering system. This paper proposes a hybrid method of the named entity recognition which combines maximum entropy model, neural network, and pattern-selection rules. The maximum entropy model is used for the proper treatment of unknown words, and neural network for disambiguation. The patternselection rules are used for the target word selection and for grouping of adjacent words. We use the data only from a training corpus and a domainindependent named entity dictionary so that our system, it is predicted, is applicable in any other domain. In addition, since each module of our system is independent, a new method can be easily adopted for executing each module.

• Type: Conference
• Year: 2001
• Where: NLPRS
• Authors: Choong-Nyoung Seon, Youngjoong Ko, Jeong-Seok Kim, Jungyun Seo
• Keywords: Maximum Entropy Model, Named Entity Recognition, Natural Language Processing, Neural Network, NLPRS 2001