The Cluster-Abstraction Model: Unsupervised Learning of Topic Hierarchies from Text Data
Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence. Machine Learning. Pages 682-686.
Abstract
This paper presents a novel statistical latent class model for text mining and interactive information access. The described learning architecture, called Cluster-Abstraction Mode/ (CAM), is purely data driven and utilizes context-specific word occurrence statistics. In an intertwined fashion, the CAM extracts hierarchical relations between groups of documents as well as an abstractive organization of keywords. An annealed version of the Expectation-Maximization (EM) algorithm for maximum likelihood estimation of the model parameters is derived. The benefits of the CAM for interactive retrieval and automated cluster summarization are investigated experimentally.
@inproceedings{ijcai1999p97,
title = {The Cluster-Abstraction Model: Unsupervised Learning of Topic Hierarchies from Text Data},
author = {Hofmann, Thomas},
booktitle = {Proceedings of the Sixteenth International Joint Conference on
Artificial Intelligence, {IJCAI-99}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Thomas Dean},
pages = {682--686},
year = {1999},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1999/97},
}
TY - CPAPER TI - The Cluster-Abstraction Model: Unsupervised Learning of Topic Hierarchies from Text Data AU - Hofmann, Thomas T2 - Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI-99 PB - International Joint Conferences on Artificial Intelligence PY - 1999 SP - 682 EP - 686 UR - https://ijcai.org/proceedings/1999/97 ER -Copied
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