The Journal of Machine Learning Research
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
NLTK: the Natural Language Toolkit
ETMTNLP '02 Proceedings of the ACL-02 Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics - Volume 1
Topic modeling: beyond bag-of-words
ICML '06 Proceedings of the 23rd international conference on Machine learning
Unsupervised prediction of citation influences
Proceedings of the 24th international conference on Machine learning
Proceedings of the 25th international conference on Machine learning
Incorporating domain knowledge into topic modeling via Dirichlet Forest priors
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Efficient methods for topic model inference on streaming document collections
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Which side are you on?: identifying perspectives at the document and sentence levels
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Automatic evaluation of topic coherence
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Not-so-latent dirichlet allocation: collapsed Gibbs sampling using human judgments
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
Holistic sentiment analysis across languages: multilingual supervised latent Dirichlet allocation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Incorporating lexical priors into topic models
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Topic extraction based on prior knowledge obtained from target documents
ACL '12 Proceedings of ACL 2012 Student Research Workshop
Aspect extraction through semi-supervised modeling
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Efficient tree-based topic modeling
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Discovering coherent topics using general knowledge
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Leveraging multi-domain prior knowledge in topic models
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
| Hi-index | 0.00 |
Topic models have been used extensively as a tool for corpus exploration, and a cottage industry has developed to tweak topic models to better encode human intuitions or to better model data. However, creating such extensions requires expertise in machine learning unavailable to potential end-users of topic modeling software. In this work, we develop a framework for allowing users to iteratively refine the topics discovered by models such as latent Dirichlet allocation (LDA) by adding constraints that enforce that sets of words must appear together in the same topic. We incorporate these constraints interactively by selectively removing elements in the state of a Markov Chain used for inference; we investigate a variety of methods for incorporating this information and demonstrate that these interactively added constraints improve topic usefulness for simulated and actual user sessions.