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.
KeywordsPragmatics RDF Triplets Ontology Information retrieval Linguistics Semantics Indexing
This work has been funded by the Department of Science and Technology (DST), Govt. of India, under the grants No. SRC/CSI/153/2011.
- 1.Baeza-Yates, R, A., Ribeiro-Neto, B, A.: Modern Information Retrieval. ACM Press/Addison-Wesley, (1999).Google Scholar
- 2.Deerwester, S, C,. et al.: Indexing by latent semantic analysis. Journal of the American Society for Information Science and Technology – JASIS, Vol. 41(6), 391–407 (1990).Google Scholar
- 3.H. Chen.: Machine learning for information retrieval: Neural Networks, Symbolic learning, and genetic algorithms. Journal of the American Society for Information Science and Technology – JASIS, Vol. 46(3), 194–216 (1995).Google Scholar
- 4.Thomas Hofmann.: Probabilistic latent semantic indexing. In: International conference SIGIR ‘99, ACM, New York, NY, USA, 50–57 (1999).Google Scholar
- 5.Dumais, Susan, Michele Banko, Eric Brill, Jimmy Lin, and Andrew Ng.: Web question answering: Is more always better?. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, ACM, 291–298 (2002).Google Scholar
- 6.Boris Katz.: START: Natural Language Question Answering System. (1993), http://start.csail.mit.edu/index.php.
- 7.Boris Katz, Gary Borchardt and Sue Felshin.: Natural Language Annotations for Question Answering. In: 19th International FLAIRS Conference (FLAIRS 2006), Melbourne Beach, FL, (2006).Google Scholar
- 8.Partee, B, H.: Introduction to Formal Semantics and Compositionality. (2013).Google Scholar
- 9.Suryanarayana, D., Hussain, S, M,. Kanakam, P., Gupta, S.: Stepping towards a semantic web search engine for accurate outcomes in favor of user queries: Using RDF and ontology technologies. 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, India, 1–6 (2015).Google Scholar
- 10.Mahaboob Hussain, S., Suryanarayana, D., Kanakam, P., Gupta, S.: Palazzo Matrix Model: An approach to simulate the efficient semantic results in search engines. In: IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2015, Coimbatore, 1–6 (2015).Google Scholar