===== Automatic Domain Adaptation for Parsing =====
Comments by : Loganathan

===== 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. 

===== Comments =====
Some of the comments and doubts arised during the discussion
   * It was asked how the data was collected, mainly due to the size of the data used in training. 
   * Training and testing were reported in the development set not on the parsing models.
   * It was noted that the parser has been tested across various domains.
   * Entropy feature was not clear. 
   * 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: =====
   * 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.
   * Inclusion of self trained corpora helped avoiding data sparsity in small corporas.

===== What do we dislike about the paper: =====
   *  Results (just before section 7) could have been better explained.