Foundations of statistical natural language processing
Foundations of statistical natural language processing
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Topic modeling: beyond bag-of-words
ICML '06 Proceedings of the 23rd international conference on Machine learning
Automatic labeling of multinomial topic models
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
The design, implementation, and use of the Ngram statistics package
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
Citation author topic model in expert search
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
ACL '12 Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries
Academic network analysis: a joint topic modeling approach
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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In this paper, we propose a multiword-enhanced author topic model that clusters authors with similar interests and expertise, and apply it to an information retrieval system that returns a ranked list of authors related to a keyword. For example, we can retrieve Eugene Charniak via search for statistical parsing. The existing works on author topic modeling assume a "bag-of-words" representation. However, many semantic atomic concepts are represented by multiwords in text documents. This paper presents a pre-computation step as a way to discover these multiwords in the corpus automatically and tags them in the term-document matrix. The key advantage of this method is that it retains the simplicity and the computational efficiency of the unigram model. In addition to a qualitative evaluation, we evaluate the results by using the topic models as a component in a search engine. We exhibit improved retrieval scores when the documents are represented via sets of latent topics and authors.