Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
The Geometry of Information Retrieval
The Geometry of Information Retrieval
A basis for information retrieval in context
ACM Transactions on Information Systems (TOIS)
Semantic Vector Combinations and the Synoptic Gospels
QI '09 Proceedings of the 3rd International Symposium on Quantum Interaction
The Quantum Probability Ranking Principle for 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
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Soft cardinality + ML: learning adaptive similarity functions for cross-lingual textual entailment
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Modeling latent topic interactions using quantum interference for information retrieval
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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The main goal of this paper is to explore latent topic analysis (LTA), in the context of quantum information retrieval. LTA is a valuable technique for document analysis and representation, which has been extensively used in information retrieval and machine learning. Different LTA techniques have been proposed, some based on geometrical modeling (such as latent semantic analysis, LSA) and others based on a strong statistical foundation. However, these two different approaches are not usually mixed. Quantum information retrieval has the remarkable virtue of combining both geometry and probability in a common principled framework. We built on this quantum framework to propose a new LTA method, which has a clear geometrical motivation but also supports a well-founded probabilistic interpretation. An initial exploratory experimentation was performed on three standard data sets. The results show that the proposed method outperforms LSA on two of the three datasets. These results suggests that the quantum-motivated representation is an alternative for geometrical latent topic modeling worthy of further exploration.