Difference between revisions of "Bo Pang Sujith Ravi 2012 Revisiting the Predictability of Language: Response Completion in Social Media"
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2) The specific context provided by the original message. | 2) The specific context provided by the original message. | ||
| − | [TM] | + | The author used the following things to model the part. |
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| + | [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] | ||
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| + | [Selection model]To select a token in stimulus uniformly at random. | ||
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| + | [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. | ||
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| + | |||
| + | The author used a linear combination to mixture these two factors (models). | ||
== Evaluation == | == Evaluation == | ||
Revision as of 20:24, 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).
