Difference between revisions of "Bo Pang Sujith Ravi 2012 Revisiting the Predictability of Language: Response Completion in Social Media"
| Line 11: | Line 11: | ||
| − | 1) The language used in responses (By using Language Model[LM](bigram model & trigram model(both back-off to unigram))) | + | 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. | + | 2) The specific '''context''' provided by the original message. |
The author used the following things to model the part. | 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] | + | '''[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. | + | '''[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. | + | '''[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). | + | The author used a '''linear''' combination to mixture these two factors (models). |
== Evaluation == | == Evaluation == | ||
Revision as of 21:25, 26 September 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 propose a method for automatic response completion 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).
