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courses:rg:automatic-domain-adaptation-for-parsing1 [2011/09/14 18:40] 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 ===== | ||
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| * Training and testing were reported in the development set not on the parsing models. | * 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. | * It was noted that the parser has been tested across various domains. | ||
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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: |
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