Ranking parser for a natural language processing system
First Claim
1. A method of determining language usage probabilities of a natural language based upon a training corpus, the method comprising:
- examining a training corpus, wherein such corpus includes phrases parsed in accordance with a set of grammar rules;
computing probabilities of usage of combinations of linguistic features based upon empirical tracking of appearances of instances of such combinations in phrases within the training corpus;
wherein the combinations of linguistic features consist of;
(transition, headword, phrase level, syntactic history, segtype);
(headword, phrase level, syntactic history, segtype);
(modifying headword, transition, headword);
or (transition, headword).
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Abstract
A natural language parse ranker of a natural language processing (NLP) system employs a goodness function to rank the possible grammatically valid parses of an utterance. The goodness function generates a statistical goodness measure (SGM) for each valid parse. The parse ranker orders the parses based upon their SGM values. It presents the parse with the greatest SGM value as the one that most likely represents the intended meaning of the speaker. The goodness function of this parse ranker is highly accurate in representing the intended meaning of a speaker. It also has reasonable training data requirements. With this parse ranker, the SGM of a particular parse is the combination of all of the probabilities of each node within the parse tree of such parse. The probability at a given node is the probability of taking a transition (“grammar rule”) at that point. The probability at a node is conditioned on highly predicative linguistic phenomena, such as “phrase levels,” “null transitions,” and “syntactic history”
71 Citations
37 Claims
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1. A method of determining language usage probabilities of a natural language based upon a training corpus, the method comprising:
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examining a training corpus, wherein such corpus includes phrases parsed in accordance with a set of grammar rules;
computing probabilities of usage of combinations of linguistic features based upon empirical tracking of appearances of instances of such combinations in phrases within the training corpus;
wherein the combinations of linguistic features consist of;
(transition, headword, phrase level, syntactic history, segtype);
(headword, phrase level, syntactic history, segtype);
(modifying headword, transition, headword);
or(transition, headword). - View Dependent Claims (2, 3)
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4. A method for determining a probability at a node in a parse tree, the method comprising:
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receiving language-usage probabilities based upon appearances of instances of combinations of linguistic features within a training corpus;
calculating the probability at the node based upon linguistic features of the node and the language-usage probabilities;
wherein the combinations of linguistic features consist of;
(transition, headword, phrase level, syntactic history, segtype);
(headword, phrase level, syntactic history, segtype);
(modifying headword, transition, headword);
or(transition, headword). - View Dependent Claims (5, 6, 7)
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8. A method for determining a statistical goodness measure (SGM) of a parse tree representing a parse of a phrase, the parse tree comprising one or more nodes, the method comprising calculating a statistical product of probabilities of each node in the parse tree, wherein the calculating comprises:
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receiving language-usage probabilities based upon appearances of instances of combinations of linguistic features within a training corpus;
calculating the probability at the node based upon linguistic features of the node and the language-usage probabilities. - View Dependent Claims (9)
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10. A method for determining a statistical goodness measure (SGM) of a parse tree representing a parse of a phrase, the parse tree comprising one or more nodes, the method comprising:
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combining probabilities of each node in the parse tree, wherein the probabilities of each node are determined by the steps comprising;
receiving language-usage probabilities based upon appearances of instances of combinations of linguistic features within a training corpus;
calculating the probabilities of each node based upon linguistic features of each node and the language-usage probabilities;
wherein the combinations of linguistic features comprises;
(transition, headword, phrase level, syntactic history, segtype);
(headword, phrase level, syntactic history, segtype);
(modifying headword, transition, headword); and
(transition, headword). - View Dependent Claims (11, 12, 15, 16)
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13. A method for determining a statistical goodness measure (SGM) of a parse tree representing a parse of a phrase, the parse tree comprising one or more nodes, the method comprising:
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combining probabilities of each node in the parse tree, wherein the probabilities of each node are determined by the steps comprising;
receiving language-usage probabilities based upon appearances of instances of combinations of linguistic features within a training corpus;
calculating the probabilities of each node based upon linguistic features of each node and the language-usage probabilities;
wherein during the combining, the probabilities of each node in the parse tree are combined in a top-down, generative approach.
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14. A method for determining statistical goodness measures (SGMs) of multiple parse trees, each tree representing a syntactically valid parse of a phrase, the method comprising determining a SGM of each parse tree representing a parse of a phrase, the parse tree comprising one or more nodes, the determining comprising:
combining probabilities of each node in the parse tree, wherein the probabilities of each node are determined by the steps comprising;
receiving language-usage probabilities based upon appearances of instances of combinations of linguistic features within a training corpus;
calculating the probabilities of each node based upon linguistic features of each node and the language-usage probabilities.
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17. A method of parsing a phrase to facilitate processing of such phrase by a computer, the method comprising:
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generating at least one parse tree representing a syntactically valid parse of the phrase, wherein the parse tree has hierarchical nodes;
calculating a syntactic history for each node. - View Dependent Claims (18, 19, 20)
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21. A computer-readable storage medium having computer-executable instructions that, when executed by a computer, determine language usage probabilities of a natural language based upon a training corpus, the method comprising:
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examining a training corpus, wherein such corpus includes phrases parsed in accordance with a set of grammar rules;
computing probabilities of usage of combinations of linguistic features based upon empirical tracking of appearances of instances of such combinations in phrases within the training corpus.
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22. A computer-readable storage medium having computer-executable instructions that, when executed by a computer, perform a method to determine a probability at a node in a parse tree, the method comprising:
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receiving language-usage probabilities based upon appearances of instances of combinations of linguistic features within a training corpus;
calculating the probability at the node based upon linguistic features of the node and the language-usage probabilities.
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23. A computer-readable storage medium having computer-executable instructions that, when executed by a computer, perform a method to determine a statistical goodness measure (SGM) of a parse tree representing a parse of a phrase, the parse tree comprising one or more nodes, the method comprising:
combining probabilities of each node in the parse tree, wherein the probabilities of each node are determined by the steps comprising;
receiving language-usage probabilities based upon appearances of instances of combinations of linguistic features within a training corpus;
calculating the probabilities of each node based upon linguistic features of each node and the language-usage probabilities.
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24. A computer-readable storage medium having computer-executable instructions that, when executed by a computer, perform a method to parse a phrase, the method comprising:
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generating at least one parse tree representing a syntactically valid parse of the phrase, wherein the parse tree has hierarchical nodes;
calculating a syntactic history for each node.
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25. An apparatus comprising:
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a processor;
a natural-language-usage probability determiner executable on the processor to;
examine a training corpus, wherein such corpus includes phrases parsed in accordance with a set of grammar rules;
compute probabilities of usage of combinations of linguistic features based upon empirical tracking of appearances of instances of such combinations in phrases within the training corpus;
wherein the combinations of linguistic features consist of;
(transition, headword, phrase level, syntactic history, segtype);
(headword, phrase level, syntactic history, segtype);
(modifying headword, transition, headword);
or(transition, headword).
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26. An apparatus comprising:
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a processor;
a natural-language-usage probability determiner executable on the processor to;
receive language-usage probabilities based upon appearances of instances of combinations of linguistic features within a training corpus;
calculate a probability at a node in a parse tree based upon linguistic features of the node and the language-usage probabilities. wherein the combinations of linguistic features consist of;
(transition, headword, phrase level, syntactic history, segtype);
(headword, phrase level, syntactic history, segtype);
(modifying headword, transition, headword);
or(transition, headword). - View Dependent Claims (27, 28)
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29. An apparatus comprising:
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a processor;
a natural-language-usage parser executable on the processor to;
generating at least one parse tree representing a syntactically valid parse of the phrase, wherein the parse tree has hierarchical nodes;
calculating a syntactic history for each node. - View Dependent Claims (30)
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31. A natural-language-usage probability determiner comprising:
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data-acquisition device for receiving language-usage probabilities based upon appearances of instances of combinations of linguistic features within a training corpus;
probability calculator for calculating a probability at a node of a parse tree based upon linguistic features of the node and the language-usage probabilities.
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32. A data structure for use with a computer having a processor and a memory, said structure comprising:
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a corpus comprising one or more phrases in a natural language;
parse trees having hierarchical nodes, each tree representing at least one syntactically valid parse of each phrase in a subset of the corpus;
wherein one or more nodes have a syntactic history associated therewith. - View Dependent Claims (33)
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34. A data structure for use with a computer having a processor and a memory, said structure comprising:
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a corpus comprising one or more phrases in a natural language;
parse trees having hierarchical nodes, each tree representing at least one syntactically valid parse of each phrase in a subset of the corpus;
wherein each node as an associated probability, wherein the associated probability of a node is based upon linguistic features of such node and language-usage probabilities derived from appearances of instances of combinations of linguistic features within a training corpus;
wherein PredParamRule Probability formula is used to calculate a probability associated with a node. - View Dependent Claims (35, 36, 37)
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Specification