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• W2144611143 abstract "Solutions for many natural language processing problems such as speech recognition, transliteration, and translation have been described as weighted finite-state transducer cascades. The transducer formalism is very useful for researchers, not only for its ability to expose the deep similarities between seemingly disparate models, but also because expressing models in this formalism allows for rapid implementation of real, data-driven systems. Finite-state toolkits can interpret and process transducer chains using generic algorithms and many real-world systems have been built using these toolkits. Current research in NLP makes use of syntax-rich models that are poorly suited to extant transducer toolkits, which process linear input and output. Tree transducers can handle these models, and a weighted tree transducer toolkit with appropriate generic algorithms will lead to the sort of gains in syntax-based modeling that were achieved with string transducer toolkits. This thesis presents Tiburon, a weighted tree transducer toolkit that is highly suited for the processing of syntactic NLP models. Tiburon contains implementations of novel determinization, minimization, and training algorithms that are extensions of classical algorithms suitable for the processing of weighted tree transducers and automata. Tiburon also contains algorithms reflecting previously known results for generation, composition, and projection. We show Tiburon’s effectiveness at rapid reproduction of previously presented syntax-based machine translation and parsing models as well as its utility in constructing and evaluating novel models." @default.
• W2144611143 created "2016-06-24" @default.
• W2144611143 creator A5000874697 @default.
• W2144611143 date "2008-01-01" @default.
• W2144611143 modified "2023-09-27" @default.
• W2144611143 title "A Weighted Tree Transducer Toolkit for Syntactic Natural Language Processing Models" @default.
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