Dependency Parsing with Transformed Feature
AbstractDependency parsing is an important subtask of natural language processing. In this paper, we propose an embedding feature transforming method for graph-based parsing, transform-based parsing, which directly utilizes the inner similarity of the features to extract information from all feature strings including the un-indexed strings and alleviate the feature sparse problem. The model transforms the extracted features to transformed features via applying a feature weight matrix, which consists of similarities between the feature strings. Since the matrix is usually rank-deficient because of similar feature strings, it would influence the strength of constraints. However, it is proven that the duplicate transformed features do not degrade the optimization algorithm: the margin infused relaxed algorithm. Moreover, this problem can be alleviated by reducing the number of the nearest transformed features of a feature. In addition, to further improve the parsing accuracy, a fusion parser is introduced to integrate transformed and original features. Our experiments verify that both transform-based and fusion parser improve the parsing accuracy compared to the corresponding feature-based parser. View Full-Text
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Wu, F. Dependency Parsing with Transformed Feature. Information 2017, 8, 13.
Wu F. Dependency Parsing with Transformed Feature. Information. 2017; 8(1):13.Chicago/Turabian Style
Wu, Fuxiang. 2017. "Dependency Parsing with Transformed Feature." Information 8, no. 1: 13.
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