Named Entity Recognition for IDEAL

The term “Named Entity”, which was first introduced by Grishman and Sundheim, is widely used in Natural Language Processing (NLP). The researchers were focusing on the information extraction task, that is extracting structured information of company activities and defense related activities from unstructured text, such as newspaper articles. The essential part of “Named Entity” is to recognize information elements, such as location, person, organization, time, date, money, percent expression, etc. To identify these entities from unstructured text, some researchers called this sub-task of information extraction as “Named Entity Recognition” (NER). Now, NER technology has become mature and there are good tools to implement this task, such as the Stanford Named Entity Recognizer (SNER), Illinois Named Entity Tagger (INET), Alias-i LingPipe (LIPI), and OpenCalasi (OCWS). Each of these has some advantages and is designed for some special data. In this term project, our final goal is to build a NER module for the IDEAL project based on a particular NER tool, such as SNER, to apply NER to the Twitter and web pages data sets. This project report presents our work towards this goal, including literature review, requirements, algorithm, development plan, system architecture, implementation, user manual, and development manual. Further, results are given with regard to multiple collections, along with discussion and plans for the future.