Proposal for Integration of NLP, Thesaurus, and Ontology on Multi Steps Natural Language Processing

Abstract

On some application systems utilizing combination of Natural Language Processing (NLP), Thesaurus, and/or Ontology, there are many problems on processing knowledge base of the systems. Text mining, Spoken Dialog System, or Document Classifier can be enumerated as examples of such applications. On processing of those applications, steps of technologies are utilized in same time as a flow of analytics. In that time, some words can be found in Ontology, but not in NLP. This symptom causes processing failure. The purpose of our research is reducing the error rate of the Multi Steps Natural Language Processing. From our investigation, about 60% of nouns cannot be found on WordNet and about 70% cannot be found on DBPedia even if it is extracted in latest NLP when tested using BTSJ dialog corpus data. On the other hand, 260 combined words on WordNet and more than 1,300 combined words on DBPedia can be found even if NLP cannot extract them as nouns. Reducing these differences between processes is important to improve accuracy of language processing. This paper proposes creating a framework to integrate dictionary data for each processor, effectiveness, and its possibility of implementation.