View source for Cross-Lingual Mixture Model for Sentiment Classification, Xinfan Meng, Furu Wei, Xiaohua Liu, Ming Zhou, Ge Xu, Houfeng Wang, ACL 2012

== 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[http://www.aclweb.org/anthology-new/P/P12/P12-1060.pdf]

== Summary ==



== Evaluation ==

The author evaluate CLMM's performance using [[http://malt.ml.cmu.edu/mw/index.php/Dataset:MPQA MPQA]] and [[http://malt.ml.cmu.edu/mw/index.php/NTCIR-6_Opinion 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: [http://en.wikipedia.org/wiki/Language_model]

Machine Translation, IBM Model-1 [http://acl.ldc.upenn.edu/J/J93/J93-2003.pdf]

LDA [http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation]


== Data Set==