Discovering information flow suing high dimensional conceptual space
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Conceptual Spaces: The Geometry of Thought
Conceptual Spaces: The Geometry of Thought
Rapid and brief communication: Further results on the subspace distance
Pattern Recognition
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
A Quantum-Based Model for Interactive Information Retrieval
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
What can quantum theory bring to information retrieval
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Introducing scalable quantum approaches in language representation
QI'11 Proceedings of the 5th international conference on Quantum interaction
Filtering documents with subspaces
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
On using a quantum physics formalism for multidocument summarization
Journal of the American Society for Information Science and Technology
Connecting the dots: mass, energy, word meaning, and particle-wave duality
QI'12 Proceedings of the 6th international conference on Quantum Interaction
| Hi-index | 0.00 |
Semantic Space models, which provide a numerical representation of words' meaning extracted from corpus of documents, have been formalized in terms of Hermitian operators over real valued Hilbert spaces by Bruza et al. [1]. The collapse of a word into a particular meaning has been investigated applying the notion of quantum collapse of superpositional states [2]. While the semantic association between words in a Semantic Space can be computed by means of the Minkowski distance [3] or the cosine of the angle between the vector representation of each pair of words, a new procedure is needed in order to establish relations between two or more Semantic Spaces. We address the question: how can the distance between different Semantic Spaces be computed? By representing each Semantic Space as a subspace of a more general Hilbert space, the relationship between Semantic Spaces can be computed by means of the subspace distance. Such distance needs to take into account the difference in the dimensions between subspaces. The availability of a distance for comparing different Semantic Subspaces would enable to achieve a deeper understanding about the geometry of Semantic Spaces which would possibly translate into better effectiveness in Information Retrieval tasks.