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
Lucene in Action (In Action series)
Lucene in Action (In Action series)
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
Evaluating topic models for information retrieval
Proceedings of the 17th ACM conference on Information and knowledge management
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Probabilistic models of ranking novel documents for faceted topic retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
VideoCLEF 2008: ASR classification with Wikipedia categories
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
A cocktail approach to the VideoCLEF'09 linking task
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Metadata enrichment for news video retrieval: a graph-based propagation approach
Proceedings of the 21st ACM international conference on Multimedia
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Determining the topic of a news video story (NVS) from its audio-visual footage is an important part of meta-data generation. In this paper we propose a news story topic modeling approach that takes advantage of online knowledge resources like Wikipedia to model the topic of a news story. A NVS is modeled as a distribution over several Wikipedia pages related to the story. The mapping of the NVS to a Wikipedia page table-of-contents (TOC) is also determined. The specific advantages of this topic modeling approach are. (1) The topic is interpretable as a weighted distribution over a set of semantically meaningful story title phrases instead of just being a collection of words. (2) It facilitates organizing news video stories as a taxonomy that captures several perspectives to the story. (3) The taxonomy facilitates exploration and non-linear search. Performance evaluations from an information extraction perspective validate the efficacy of the proposed topic modeling approach compared to TF-IDF and LDA based approaches on a large news video corpus.