Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/78363
Title: Summarization of corporate risk factor disclosure through topic modeling
Authors: Bao, Y.
Datta, A. 
Keywords: Risk factor disclosure
Summarization
Topic modeling
Variational EM
Issue Date: 2012
Citation: Bao, Y.,Datta, A. (2012). Summarization of corporate risk factor disclosure through topic modeling. International Conference on Information Systems, ICIS 2012 1 : 701-719. ScholarBank@NUS Repository.
Abstract: In this paper, we propose a novel problem of summarizing textual corporate risk factor disclosure, which aims to simultaneously infer the risk types across corpus and assign each risk factor to its most probable risk type. To solve the problem, we develop a variation of LDA topic model called Sent-LDA. The variational EM learning algorithm, which guarantees fast convergence, is derived and implemented for our model. Experiments show that our model is much more efficient and effective than LDA for solving our proposed problem. Specifically, our model is 50 times faster than LDA in the same conditions, and generates better topics for summarization than LDA. Our model is visualized in a publicly available system.
Source Title: International Conference on Information Systems, ICIS 2012
URI: http://scholarbank.nus.edu.sg/handle/10635/78363
ISBN: 9781627486040
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

79
checked on Oct 20, 2018

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.