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- W2161163246 abstract "The paper presents a hidden vector state (HVS) model for hierarchical semantic parsing. The model associates each state of push-down automata with the state of an HMM. State transitions are factored into separate stack pop and push operations and then constrained to give a tractable search space. The result is a model which is complex enough to capture hierarchical structure but which can be trained automatically from unannotated data. Experiments have been conducted on ATIS-3 1993 and 1994 test sets. The results show that the HVS model outperforms a general finite state tagger (FST) by 19% to 32% in error reduction." @default.
- W2161163246 created "2016-06-24" @default.
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- W2161163246 date "2003-11-21" @default.
- W2161163246 modified "2023-09-23" @default.
- W2161163246 title "Hidden vector state model for hierarchical semantic parsing" @default.
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- W2161163246 doi "https://doi.org/10.1109/icassp.2003.1198769" @default.
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