Holographic Reduced Representation: Distributed Representation for Cognitive Structures
Holographic Reduced Representation: Distributed Representation for Cognitive Structures
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
Combining Text Vector Representations for Information Retrieval
TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
Representing Context Information for Document Retrieval
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Concept based representations for ranking in geographic information retrieval
IceTAL'10 Proceedings of the 7th international conference on Advances in natural language processing
Computer Vision and Image Understanding
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Current representation schemes for automatic text classification treat documents as syntactically unstructured collections of words or 'concepts'. Past attempts to encode syntactic structure have treated part-of-speech information as another word-like feature, but have been shown to be less effective than non-structural approaches. We propose a new representation scheme using Holographic Reduced Representations (HRRs) as a technique to encode both semantic and syntactic structure. This method improves on previous attempts in the literature by encoding the structure across all features of the document vector while preserving text semantics. Our method does not increase the dimensionality of the document vectors, allowing for efficient computation and storage. We present classification results of our HRR text representations versus Bag-of-Concepts representations and show that our method of including structure improves text classification results.