The desire to store and the need to use electronic data has greatly increased as the power, availability, and connectivity of computers has grown. A large portion of this data is in the form of unstructured text documents. Locating specific information within this amorphous mass of documents is an area of active research. Our contribution to this pursuit is the development of the Document Entity and Resolution (DEAR) system. This system combines semantic similarity matching as provided by the open source WordNet database with the ability to recognize named entities through the OpenCalais system. When used in concert, this provides a novel way for users to quickly find relevant content and detect and identify uniquely named entities within that content. The theory behind the system is defined and the working system is described. This system is then applied to a collection of assessment documents as a proof-of-concept test of its viability. The results are promising and indicate that further research is warranted.
Sedbrook, Tod and Lightfoot, Jay M.
"DEAR: A New Technique for Information Extraction and Context-Dependent Text Mining,"
Communications of the IIMA: Vol. 10
, Article 3.
Available at: https://scholarworks.lib.csusb.edu/ciima/vol10/iss3/3