Named entity recognition and classification using context Hidden Markov Model

Named entity (NE) recognition is a core technology for understanding low level semantics of texts. In this paper we report our preliminary results for Named Entity Recognition on MUC 7 corpus by combining the supervised machine learning system in the form of probabilistic generative Hidden Markov Model (HMM) for named entity classes PERSON, ORGANIZATION and LOCATION, and grammar based component for DATE, TIME, MONEY and PERCENT. We have implemented two variations of the basic Hidden Markov Model, where the second one is our version of HMM which uses the context of surrounding words to determine the NE class of the current word, leading to more accurate and faster NE recognition.