Open Access Subscription Access
Open Access Subscription Access
Innovative Feature Selection for Effective Context Resolution Using Natural Language Query Interface
Any system that supports human interaction through natural language has high utility and ease of use. The challenge in natural language arises due to difficulty in correct interpretation, disambiguation and context resolution. Use of natural language for information retrieval and other related activities enhances effectiveness of the process and provides greater flexibility to the users in terms of document access. To do so, use of a feature vector with respect to different perspectives in addition to metadata is proposed. The work presented here encompasses a generic architecture of context resolution and categorization of document through use of natural language to achieve the intended goal. The architecture encompasses various document indices along with methodology for lexicon analysis. It also uses metadata. The proposed document features (indices) along with lexical analysis will help in correctly determining the context through the limited query keywords. The architecture is domain independent and can be used for various applications in vernacular languages. To demonstrate the application of the architecture and its methodology, necessary discussion is also included in this paper with required technical details.
Context Resolution, Lexical Analysis, Natural Language Interface, Document Features, Text Categorization.
- Alessandro Moschitti Natural language processing and automated text categorization, Ph.D.thesis, (2003).
- Andrei Mikheev, Periods, Capitalised Words, Article published at University of Edinburgh, association for computer linguistics, (2002).
- Amisha Shingala, Dr. Paresh Virparia, Enriching Document Features for Effective Information Retrieval using Natural Language Query Interface, International Journal of IT, Engineering and Applied Sciences Research, (2012).
- Barzilay and Elhadad, R. Barzilay and M. Elhadad. Using lexical chains for text summarization. In In Proceedings of the Intelligent Scalable Text Summarization Workshop (ISTS’97), ACL, Madrid,( 1997)
- Claude de Loupy, Eric Crestan, Elise Lamaire, Proper Noun Thesaurus for Document Retrieval and Question Answering, project by French government, (2001).
- David Nadeau & Santoshi Sekine, A survey of named entity recognisation and classification, National Research council Canada/New York Univeristy, (2007)
- Joanna Rabiega, Syntactic Structure of Polish Proper Names of Places, ICS PAS Warsaw, Porland, (2006)
- Marcus Hassler & G.unther Flied, Text Preparation through extended tokenization, Article published at University Klangenfurt, (2006).
- Marie-Catherine de Marneffe, Christopher D. Manning, “The Stanford typed dependencies manual” in Revised for Stanford Parser v1.6.2, ,(2010).
- Nina Wacholder et.al, Disambiguation of proper names in text in the proceedings of Natural Language processing conference, Washinton D.C, (1997)
- Ratinov, L. and Roth, D, Design challenges and misconceptions in named entity recognition (2009).
- Wacholder N, Y.Ravin, Retrieving information from full text using linguistic knowledge, in proceedings of fifteen national online meeting, New York, (1994).
Abstract Views: 37
PDF Views: 6