High-Performance Linguistics Scheme for Cognitive Information Processing

Natural language understanding is a principal segment of natural language processing in semantic analysis to the use of pragmatics to originate meaning from context. Information retrieval (IR) is one of the emerging areas to deal with enormous amounts of data, which are in the form of natural language. Content of the query posed will affect both volume of data and design of IR applications. This paper presents a cognition-applied methodology termed as High-Performance Linguistics (HPL), which is a question-answering system for interpreting a natural language sentence/query. It constitutes three phases of computations: parsing, triplet generation and triplet mapping/matching. The generation of the triplets for the knowledge base is to create new data and compare them with that of stored triplets in the database. Thus, the generation of the cognitive question-answering system can make easy using this machine learning techniques on the generated triplet database.