Cross-Lingual Mixture Model for Sentiment Classification, Xinfan Meng, Furu Wei, Xiaohua Liu, Ming Zhou, Ge Xu, Houfeng Wang, ACL 2012
Contents
Citation
Cross-Lingual Mixture Model for Sentiment Classification, Xinfan Meng, Furu Wei, Xiaohua Liu, Ming Zhou, Ge Xu, Houfeng Wang, ACL 2012
Online version
An online pdf version is here[1]
Summary
Evaluation
The author evaluate CLMM's performance using [MPQA] and [NTCIR] in mainly two cases:
1) Keep the labeled data in target language (Chinese) unavailable.
A
2) Using the labeled target language (Chinese) data.
B
Discussion
The author provides an analysis (entropy estimates along with upper-bound numbers observed from experiments) and suggests that there can be interesting future work to explore the contextual information provided by the stimulus more effectively and further improve the response completion task.
Related papers
Ritter et. al 2010, Data-Driven Response Generation in Social Media
Regina Barzilay and Mirella Lapata. 2005, Modeling local coherence: An entity-based approach
Study plan
Language Model: [2]
Machine Translation, IBM Model-1 [3]
LDA [4]
