TY - GEN
T1 - IMS-DTM
T2 - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
AU - Chen, Xilun
AU - Candan, Kasim
AU - Sapino, Maria Luisa
N1 - Funding Information:
∗This work is partially funded by NSF grants #1610282, #1633381, #1318788, #1339835 and also supported in part by the NSF I/UCRC through the NSF grant #0856090. Copyright ©c 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Publisher Copyright:
Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2018
Y1 - 2018
N2 - Dynamic topic models (DTM) are commonly used for mining latent topics in evolving web corpora. In this paper, we note that a major limitation of the conventional DTM based models is that they assume a predetermined and fixed scale of topics. In reality, however, topics may have varying spans and topics of multiple scales can co-exist in a single web or social media data stream. Therefore, DTMs that assume a fixed epoch length may not be able to effectively capture latent topics and thus negatively affect accuracy. In this paper, we propose a Multi-Scale Dynamic Topic Model (MS-DTM) and a complementary Incremental Multi-Scale Dynamic Topic Model (IMS-DTM) inference method that can be used to capture latent topics and their dynamics simultaneously, at different scales. In this model, topic specific feature distributions are generated based on a multi-scale feature distribution of the previous epochs; moreover, multiple scales of the current epoch are analyzed together through a novel multi-scale incremental Gibbs sampling technique. We show that the proposed model significantly improves efficiency and effectiveness compared to the single scale dynamic DTMs and prior models that consider only multiple scales of the past.
AB - Dynamic topic models (DTM) are commonly used for mining latent topics in evolving web corpora. In this paper, we note that a major limitation of the conventional DTM based models is that they assume a predetermined and fixed scale of topics. In reality, however, topics may have varying spans and topics of multiple scales can co-exist in a single web or social media data stream. Therefore, DTMs that assume a fixed epoch length may not be able to effectively capture latent topics and thus negatively affect accuracy. In this paper, we propose a Multi-Scale Dynamic Topic Model (MS-DTM) and a complementary Incremental Multi-Scale Dynamic Topic Model (IMS-DTM) inference method that can be used to capture latent topics and their dynamics simultaneously, at different scales. In this model, topic specific feature distributions are generated based on a multi-scale feature distribution of the previous epochs; moreover, multiple scales of the current epoch are analyzed together through a novel multi-scale incremental Gibbs sampling technique. We show that the proposed model significantly improves efficiency and effectiveness compared to the single scale dynamic DTMs and prior models that consider only multiple scales of the past.
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M3 - Conference contribution
AN - SCOPUS:85060435965
T3 - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
SP - 5078
EP - 5085
BT - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PB - AAAI press
Y2 - 2 February 2018 through 7 February 2018
ER -