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- W2158431714 abstract "We present twin results on Chinese semantic parsing, with application to English-Chinese cross- lingual verb frame acquisition. First, we describe two new state-of-the-art Chinese shallow semantic parsers leading to an F-score of 82.01 on simultaneous frame and argument boundary identification and labeling. Subsequently, we propose a model that applies the separate Chinese and English semantic parsers to learn cross-lingual semantic verb frame argument mappings with 89.3% accuracy. The only training data needed by this cross-lingual learning model is a pair of non-parallel monolingual Propbanks, plus an unannotated parallel corpus. We also present the first reported controlled comparison of maximum entropy and SVM approaches to shallow semantic parsing, using the Chinese data." @default.
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- W2158431714 date "2006-01-01" @default.
- W2158431714 modified "2023-10-15" @default.
- W2158431714 title "Automatic Learning of Chinese English Semantic Structure Mapping" @default.
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- W2158431714 doi "https://doi.org/10.1109/slt.2006.326797" @default.
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