TY - GEN
T1 - Online cross-lingual PLSI for evolutionary theme patterns analysis
AU - Xin, Xin
AU - Zhuang, Kun
AU - Fang, Ying
AU - Huang, Heyan
PY - 2013
Y1 - 2013
N2 - In this paper, we focus on the problem of evolutionary theme patterns (ETP) analysis in cross-lingual scenarios. Previously, cross-lingual topic models in batch mode have been explored. By directly applying such techniques in ETP analysis, however, two limitations would arise. (1) It is time-consuming to re-train all the latent themes for each time interval in the time sequence. (2) The latent themes between two adjacent time intervals might lose continuity. This motivates us to utilize online algorithms to solve these limitations. The research of online topic models is not novel, but previous work cannot be directly employed, because they mainly target at monolingual texts. Consequently, we propose an online cross-lingual topic model. By experimental verification in a real world dataset, we demonstrate that our algorithm performs well in the ETP analysis task. It can efficiently reduce the updating time complexity; and it is effective in solving the continuity limitation.
AB - In this paper, we focus on the problem of evolutionary theme patterns (ETP) analysis in cross-lingual scenarios. Previously, cross-lingual topic models in batch mode have been explored. By directly applying such techniques in ETP analysis, however, two limitations would arise. (1) It is time-consuming to re-train all the latent themes for each time interval in the time sequence. (2) The latent themes between two adjacent time intervals might lose continuity. This motivates us to utilize online algorithms to solve these limitations. The research of online topic models is not novel, but previous work cannot be directly employed, because they mainly target at monolingual texts. Consequently, we propose an online cross-lingual topic model. By experimental verification in a real world dataset, we demonstrate that our algorithm performs well in the ETP analysis task. It can efficiently reduce the updating time complexity; and it is effective in solving the continuity limitation.
KW - Cross-lingual PLSI
KW - Evolutionary topic patterns
KW - Incremental/Online PLSI
KW - Temporal text mining
UR - https://www.scopus.com/pages/publications/84893563042
U2 - 10.1007/978-3-642-37453-1_7
DO - 10.1007/978-3-642-37453-1_7
M3 - Conference contribution
AN - SCOPUS:84893563042
SN - 9783642374524
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 74
EP - 85
BT - Advances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings
PB - Springer Verlag
T2 - 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013
Y2 - 14 April 2013 through 17 April 2013
ER -