@inproceedings{42a9e2dddd20404b8213e4cccc86711d,
title = "A phrase mining framework for recursive construction of a topical hierarchy",
abstract = "A high quality hierarchical organization of the concepts in a dataset at different levels of granularity has many valuable applications such as search, summarization, and content browsing. In this paper we propose an algorithm for recursively constructing a hierarchy of topics from a collection of content-representative documents. We characterize each topic in the hierarchy by an integrated ranked list of mixed-length phrases. Our mining framework is based on a phrase-centric view for clustering, extracting, and ranking topical phrases. Experiments with datasets from different domains illustrate our ability to generate hierarchies of high quality topics represented by meaningful phrases.",
keywords = "Keyphrase extraction, Keyphrase ranking, Network analysis, Ontology learning, Topic modeling",
author = "Chi Wang and Marina Danilevsky and Nihit Desai and Yinan Zhang and Phuong Nguyen and Thrivikrama Taula and Jiawei Han",
note = "Funding Information: This work was supported in part by the U.S. National Science Foundation grants IIS-0905215, U.S. Army Research Laboratory under Cooperative Agreement No. W911NF-09-2-0053 (NS-CTA). Chi Wang was supported by a Microsoft Research PhD Fellowship. Marina Danilevsky was supported by a National Science Foundation Graduate Re- search Fellowship grant NSF DGE 07-15088. The authors wish to acknowledge the University of Illinois at Urbana- Champaign Library (http://www.library.Illinois. edu), which provided support for this research. Funding Information: This work was supported in part by the U.S. National Science Foundation grants IIS–0905215, U.S. Army Research Laboratory under Cooperative Agreement No. W911NF– 09–2–0053 (NS-CTA). Chi Wang was supported by a Microsoft Research PhD Fellowship. Marina Danilevsky was supported by a National Science Foundation Graduate Research Fellowship grant NSF DGE 07-15088. The authors wish to acknowledge the University of Illinois at Urbana-Champaign Library (http://www.library.illinois. edu), which provided support for this research. Publisher Copyright: Copyright {\textcopyright} 2013 ACM.; 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013 ; Conference date: 11-08-2013 Through 14-08-2013",
year = "2013",
month = aug,
day = "11",
doi = "10.1145/2487575.2487631",
language = "English (US)",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery",
pages = "437--445",
editor = "Rajesh Parekh and Jingrui He and Inderjit, {Dhillon S.} and Paul Bradley and Yehuda Koren and Rayid Ghani and Senator, {Ted E.} and Grossman, {Robert L.} and Ramasamy Uthurusamy",
booktitle = "KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
}