Training tree transducers for probabilistic operations
First Claim
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1. A method for performing probabilistic operations with trained tree transducers, the method comprising:
- through execution of instructions stored in memory, obtaining tree transducer information including input/output pair information and transducer information;
through execution of instructions stored in memory, converting said input/output pair information and said transducer information into a set of values in a weighted tree grammar; and
through execution of instructions stored in memory, using said weighted tree grammar to solve a problem that requires information from the input/output pair information and transducer information.
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Abstract
Tree transducers can be trained for use in probabilistic operations such as those involved in statistical based language processing. Given sample input/output pairs as training, and given a set of tree transducer rules, the information is combined to yield locally optimal weights for those rules. This combination is carried out by building a weighted derivation forest for each input/output pair and applying counting methods to those forests.
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Citations
23 Claims
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1. A method for performing probabilistic operations with trained tree transducers, the method comprising:
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through execution of instructions stored in memory, obtaining tree transducer information including input/output pair information and transducer information; through execution of instructions stored in memory, converting said input/output pair information and said transducer information into a set of values in a weighted tree grammar; and through execution of instructions stored in memory, using said weighted tree grammar to solve a problem that requires information from the input/output pair information and transducer information. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. A method for training tree transducers for probabilistic operations, the method comprising:
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using a computer to obtain information in the form of a first-tree, second information corresponding to said first tree, and transducer information; and using said computer to automatically distill information from said first tree, from said second information, and from said transducer information into a list of information in a specified tree grammar with weights associated with entries in the list and to produce locally optimal weights for said entries. - View Dependent Claims (18, 19, 20, 21, 22, 23)
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Specification