Named Entity Recognition Using Memory Based Learning Algorithms

Abstract

The information explosion has created remarkable measure of published information that is growing at an astounding rate. As information grows at this rate, managing it becomes a daunting challenge. The key to this test lays in innovation of information extractions technology that can change unstructured information into organized data to be understood and controlled by machines. Named Entity Recognition (NER) has the capability to extract named entities from unstructured documents, ordering them into pre-characterized semantic classifications and further extracts information in a specific language through use of defined entities.The research purpose was to analyze named entity recognition algorithms and use them to identify entities. Experimental research methodology employed. Kikuyu language was used to provide data for this research. This research analyzed named entity recognition using Memory Based algorithm and use them to identify entities. The data collected was used to train a memory-based tool through statistical based approach. The outcome assisted in solving semantic annotation problem, development of automatic question-answering systems, semantic web probe, and social web problem. The output was a named entity recognition that uses memory-based learning algorithm.