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courses:rg:automatic-domain-adaptation-for-parsing1 [2011/09/14 18:49]
ramasamy
courses:rg:automatic-domain-adaptation-for-parsing1 [2011/09/14 18:50] (current)
ramasamy
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 Comments by : Loganathan Comments by : Loganathan
  
-==== Objective ====+===== Objective ​=====
 The objective of the paper is to make the statistical parsers adapting to new domains. Best parsing model for a particular testing data is identified by combining training data(source mixture) from different domains. This source mixture is learned from a regression model which will identify the appropriate parsing model. ​ The objective of the paper is to make the statistical parsers adapting to new domains. Best parsing model for a particular testing data is identified by combining training data(source mixture) from different domains. This source mixture is learned from a regression model which will identify the appropriate parsing model. ​
  
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    * The idea was to successfully adapt to new domains than to achieve very good accuracy for a particular domain. ​    * The idea was to successfully adapt to new domains than to achieve very good accuracy for a particular domain. ​
  
-==== What do we like about the paper: ====+===== What do we like about the paper: ​=====
    * The multiple source adaptation method can identify the factors which affect the parsing accuracy for texts from different domains.    * The multiple source adaptation method can identify the factors which affect the parsing accuracy for texts from different domains.
    * They successfully included methods for domain detection compared to previous works.    * They successfully included methods for domain detection compared to previous works.
    * Inclusion of self trained corpora helped avoiding data sparsity in small corporas.    * Inclusion of self trained corpora helped avoiding data sparsity in small corporas.
  
-==== What do we dislike about the paper: ====+===== What do we dislike about the paper: ​=====
    ​* ​ Results (just before section 7) could have been better explained. ​    ​* ​ Results (just before section 7) could have been better explained. ​
  

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