On modeling of information retrieval concepts in vector spaces
ACM Transactions on Database Systems (TODS)
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A vector space model for automatic indexing
Communications of the ACM
Holographic Reduced Representation: Distributed Representation for Cognitive Structures
Holographic Reduced Representation: Distributed Representation for Cognitive Structures
Noun-phrase analysis in unrestricted text for information retrieval
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Using bag-of-concepts to improve the performance of support vector machines in text categorization
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Characteristics of geographic information needs
Proceedings of the 4th ACM workshop on Geographical information retrieval
Integrating structure and meaning: a new method for encoding structure for text classification
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Cross-lingual random indexing for information retrieval
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
Using contextual spaces for image re-ranking and rank aggregation
Multimedia Tools and Applications
Hi-index | 0.00 |
The bag of words representation (BoW), which is widely used in information retrieval (IR), represents documents and queries as word lists that do not express anything about context information. When we look for information, we find that not everything is explicitly stated in a document, so context information is needed to understand its content. This paper proposes the use of bag of concepts (BoC) and Holographic reduced representation (HRR) in IR. These representations go beyond BoW by incorporating context information to document representations. Both HRR and BoC are produced using a vector space methodology known as Random Indexing, and allow expressing additional knowledge from different sources. Our experiments have shown the feasibility of the representations and improved the mean average precision by up to 7% when they are compared with the traditional vector space model.