Algorithms for clustering data
Algorithms for clustering data
ACM SIGIR Forum
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Topic segmentation with an aspect hidden Markov model
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A model of textual affect sensing using real-world knowledge
Proceedings of the 8th international conference on Intelligent user interfaces
The Journal of Machine Learning Research
ConceptNet — A Practical Commonsense Reasoning Tool-Kit
BT Technology Journal
ICML '06 Proceedings of the 23rd international conference on Machine learning
LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Mining business topics in source code using latent dirichlet allocation
ISEC '08 Proceedings of the 1st India software engineering conference
Latent dirichlet allocation for tag recommendation
Proceedings of the third ACM conference on Recommender systems
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Part-of-speech tagging from 97% to 100%: is it time for some linguistics?
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
Expectation-propagation for the generative aspect model
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Communications of the ACM
Sentic Computing: Techniques, Tools, and Applications
Sentic Computing: Techniques, Tools, and Applications
Learning Topic Models -- Going beyond SVD
FOCS '12 Proceedings of the 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science
A graph-based approach to commonsense concept extraction and semantic similarity detection
Proceedings of the 22nd international conference on World Wide Web companion
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Topic modeling is a technique used for discovering the abstract 'topics' that occur in a collection of documents, which is useful for tasks such as text auto-categorization and opinion mining. In this paper, a commonsense knowledge based algorithm for document topic modeling is presented. In contrast to probabilistic models, the proposed approach does not involve training of any kind and does not depend on word co-occurrence or particular word distributions, making the algorithm effective on texts of any length and composition. 'Semantic atoms' are used to generate feature vectors for document concepts. These features are then clustered using group average agglomerative clustering, providing much improved performance over existing algorithms.