Recurrent conditional random fields
First Claim
1. A language understanding (LU) system, comprising:
- a computing device; and
a computer program having program modules executable by the computing device, the computing device being directed by the program modules of the computer program to,receive feature values corresponding a sequence of words,generate semantic labels for words in the sequence of words, said semantic label generation comprising using a recurrent conditional random field (R-CRF) comprising,a recurrent neural network (RNN) portion which generates RNN activation layer activations data that is indicative of a semantic label for a word, the RNN receiving feature values associated with a word in the sequence of words and outputting RNN activation layer activations data that is indicative of a semantic label, anda conditional random field (CRF) portion which takes as input the RNN activation layer activations data output from the RNN for one or more words in the sequence of words and outputs label data that is indicative of a separate semantic label that is to be assigned to each of the one or more words in the sequence of words associated with the RNN activation layer activations data, andassign each semantic label corresponding to the data output by the CRF portion of the R-CRF to the appropriate one said one or more words in the sequence of words.
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Abstract
Recurrent conditional random field (R-CRF) embodiments are described. In one embodiment, the R-CFR receives feature values corresponding to a sequence of words. Semantic labels for words in the sequence of words are then generated and each label is assigned to the appropriate one of the words in the sequence of words. The R-CRF used to accomplish these tasks includes a recurrent neural network (RNN) portion and a conditional random field (CRF) portion. The RNN portion receives feature values associated with a word in the sequence of words and outputs RNN activation layer activations data that is indicative of a semantic label. The CRF portion inputs the RNN activation layer activations data output from the RNN for one or more words in the sequence of words and outputs label data that is indicative of a separate semantic label that is to be assigned to each of the words.
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Citations
20 Claims
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1. A language understanding (LU) system, comprising:
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a computing device; and a computer program having program modules executable by the computing device, the computing device being directed by the program modules of the computer program to, receive feature values corresponding a sequence of words, generate semantic labels for words in the sequence of words, said semantic label generation comprising using a recurrent conditional random field (R-CRF) comprising, a recurrent neural network (RNN) portion which generates RNN activation layer activations data that is indicative of a semantic label for a word, the RNN receiving feature values associated with a word in the sequence of words and outputting RNN activation layer activations data that is indicative of a semantic label, and a conditional random field (CRF) portion which takes as input the RNN activation layer activations data output from the RNN for one or more words in the sequence of words and outputs label data that is indicative of a separate semantic label that is to be assigned to each of the one or more words in the sequence of words associated with the RNN activation layer activations data, and assign each semantic label corresponding to the data output by the CRF portion of the R-CRF to the appropriate one said one or more words in the sequence of words. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A recurrent conditional random field (R-CRF), comprising:
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a recurrent neural network (RNN) portion which generates RNN activation layer activations data that is indicative of a label for a word, the RNN receiving feature values associated with a word in the sequence of words and outputting RNN activation layer activations data that is indicative of a label, said RNN portion comprising, an input layer of nodes wherein each feature value of the feature values associated with a word are input into a different one of the input layer nodes, a hidden layer comprising nodes that receive outputs from the input layer, said outputs from the input layer being adjustably weighted, and an activation layer comprising nodes that receive outputs from the hidden layer, said outputs from the hidden layer being adjustably weighted; and a conditional random field (CRF) portion which takes as input the RNN activation layer activations data output from the activation layer of the RNN portion for words in the sequence of words and which outputs label data that is indicative of a separate label that is to be assigned to each of the words in the sequence of words associated with the RNN activation layer activations data. - View Dependent Claims (13, 14, 15, 16, 17, 18)
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19. A computer-implemented process for training a recurrent conditional random field (R-CRF) to output semantic label designations for words in a sequence of words, said R-CRF comprising a recurrent neural network (RNN) portion which outputs RNN activation layer activations data that is indicative of a semantic label for a word in response to feature values associated with that word in the sequence of words being input and which comprises a series of interconnected multi-node layers having weighted connections between layers, and a conditional random field (CRF) portion which takes as input the RNN activation layer activations data output from the RNN portion for one or more words in the sequence of words and then outputs label data that is indicative of a separate semantic label that is to be assigned to each of the one or more words in the sequence of words, said training process comprising:
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using a computing device to perform the following process actions; accessing a set of training data pair sequences, each of said training data pair sequences comprising a sequence of pairs of feature values corresponding to a word and label data that is indicative of a correct semantic label for that word; inputting each training data pair sequence of said set one by one into the R-CRF; and for each training data pair sequence input into the R-CRF, employing a CRF sequence-level objective function and a backpropagation procedure to compute adjusted weights for the connections between layers of the RNN portion of the R-CRF, and changing the weight associated with the connections between layers of the RNN portion of the R-CRF based on the computed adjusted weights. - View Dependent Claims (20)
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Specification