Cross-Lingual Mixture Model for Sentiment Classification, Xinfan Meng, Furu Wei, Xiaohua Liu, Ming Zhou, Ge Xu, Houfeng Wang, ACL 2012
Contents
Citation
Revisiting the Predictability of Language: Response Completion in Social Media, Bo Pang Sujith Ravi, EMNLP 2012
Online version
An online pdf version is here[1]
Summary
This paper proposed a method for automatic response completion in Social Media context by considering mainly two factors:
1) The language used in responses (By using Language Model[LM] (bigram model & trigram model(both back-off to unigram)))
2) The specific context provided by the original message.
The author used the following things to model the part.
[TM] Methods In Ritter et. al 2010, Data-Driven Response Generation in Social Media, which is to use a translation model to do alignment between stimulus(source) and the response(target). [IBM-Model1]
[Selection model] To select a token in stimulus uniformly at random.
[Topic model] First learn a topic model over conversations in the training data using LDA. Then identify the most likely topic of the conversation based on s, and expect responds to be generated from this topic.
The author used a linear combination to mixture these two factors (models).
Evaluation
The author claims that translation-based approach is not well suited for this particular task and LDA suffers from the fact that the text is noisy (or too generic) therefore, not useful enough to help in the prediction task.
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]
