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courses:rg:automatic-domain-adaptation-for-parsing1 [2011/09/14 18:21] 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. | ||
| ===== Comments ===== | ===== Comments ===== | ||
| Some of the comments and doubts arised during the discussion | 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. | + | * It was asked how the data was collected, mainly due to the size of the data used in training. |
| - | ==== What do we dislike about the paper: ==== | + | * 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: ===== | |
| - | ==== 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: ===== | |
| + | | ||