Enhancing Information Extraction with Context and Inference

Abstract

Natural Language Processing (NLP) provides tools to extract explicitly stated information from text documents. These tools include Named Entity Recognition (NER) and Parts-Of-Speech (POS). The extracted information represents discrete entities in the text and some relationships that may exist among them. To perform intelligent analysis on the extracted information a context has to exist in which this information is placed. The context provides an environment to link information that is extracted from multiple documents and offers a big picture of the domain. Analysis can then be provided by adding inference capabilities to the environment. The ODIX platform provides an environment for bringing together information extraction, ontology, and intelligent analysis. The platform design relies on existing NLP tools to provide the information extraction capabilities. It also utilizes a Web crawler to collect text documents from the Web. The context is provided by a domain ontology that is loaded at run time. The ontology offers limited inference capabilities and external intelligent agents offer more advanced reasoning capabilities. User involvement is key to the success of the analysis process. At every step of the process, the user has the opportunity to direct the system, set selection criteria, correct errors, or add additional information.