Automatic text processing
Lexical analysis and stoplists
Information retrieval
Information retrieval
Information retrieval
Commercial applications of natural language processing
Communications of the ACM
Communications of the ACM
Extending the database relational model to capture more meaning
ACM Transactions on Database Systems (TODS)
A vector space model for automatic indexing
Communications of the ACM
Natural Language Information Processing: A Computer Grammmar of English and Its Applications
Natural Language Information Processing: A Computer Grammmar of English and Its Applications
Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Implementation of the SMART Information Retrieval System
Implementation of the SMART Information Retrieval System
Academic conference homepage understanding using constrained hierarchical conditional random fields
Proceedings of the 17th ACM conference on Information and knowledge management
Towards a comprehensive call ontology for Research 2.0
i-KNOW '11 Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies
Speculative originality and optimality in knowledge development infrastructures
Proceedings of the 2012 iConference
Hi-index | 0.00 |
In many domains there are specific attributes in documents that carry more weight than the general words in the document. This paper proposes the use of information extraction techniques in order to identify these attributes for the domain of calls for papers. The utilisation of attributes into queries imposes new requirements on the retrieval method of conventional information retrieval systems. A new model for estimating the relevance of documents to user requests is also presented. The effectiveness of this model and the benefits of integrating information extraction with information retrieval are shown by comparing our system with a typical information retrieval system. The results show a precision increase of between 45% and 60% of all recall points.