Method and device for extracting attributes associated with centre of interest from natural language sentences
First Claim
1. A method for extracting attributes associated with Center of Interest (COI) from natural language sentences, the method comprising:
- creating, by a COI attribute processing device, an input vector comprising a plurality of parameters for each target word in a sentence inputted by a user, wherein the plurality of parameters for each target word comprise a Part of Speech (POS) vector associated with the target word and at least two words preceding the target word, a word embedding for the target word, a word embedding for a head word of the target word in a dependency parse tree for the sentence, and a dependency label for the target word;
processing for each target word, by the COI attribute processing device, the input vector through a trained bidirectional Gated Recurrent Unit (GRU) neural network, wherein the trained bidirectional GRU neural network is trained to identify attributes associated with COI from a plurality of sentences, and wherein attributes associated with a COI in a sentence augment the context of the COI;
associating, by the COI attribute processing device, COI attribute tags to each target word in the sentence based on processing of associated input vector through the trained bidirectional GRU neural network;
extracting, by the COI attribute processing device, attributes from the sentence based on the COI attribute tags associated with each target word in the sentence; and
providing, by the COI attribute processing device, a response to the sentence inputted by the user based on the attributes extracted from the sentence.
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Abstract
A method and device for extracting attributes associated with Center of Interest (COI) from natural language sentences is disclosed. The method includes creating an input vector comprising a plurality of parameters for each target word in a sentence inputted by a user. The method further includes processing for each target word, the input vector through a trained bidirectional GRU neural network, which is trained to identify attributes associated with COI from a plurality of sentences. The method includes associating COI attribute tags to each target word in the sentence based on processing of associated input vector through the trained bidirectional GRU neural network. The method further includes extracting attributes from the sentence based on the COI attribute tags associated with each target word in the sentence. The method further includes providing a response to the sentence inputted by the user based on the attributes extracted from the sentence.
10 Citations
17 Claims
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1. A method for extracting attributes associated with Center of Interest (COI) from natural language sentences, the method comprising:
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creating, by a COI attribute processing device, an input vector comprising a plurality of parameters for each target word in a sentence inputted by a user, wherein the plurality of parameters for each target word comprise a Part of Speech (POS) vector associated with the target word and at least two words preceding the target word, a word embedding for the target word, a word embedding for a head word of the target word in a dependency parse tree for the sentence, and a dependency label for the target word; processing for each target word, by the COI attribute processing device, the input vector through a trained bidirectional Gated Recurrent Unit (GRU) neural network, wherein the trained bidirectional GRU neural network is trained to identify attributes associated with COI from a plurality of sentences, and wherein attributes associated with a COI in a sentence augment the context of the COI; associating, by the COI attribute processing device, COI attribute tags to each target word in the sentence based on processing of associated input vector through the trained bidirectional GRU neural network; extracting, by the COI attribute processing device, attributes from the sentence based on the COI attribute tags associated with each target word in the sentence; and providing, by the COI attribute processing device, a response to the sentence inputted by the user based on the attributes extracted from the sentence. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A Center of Interest (COI) attribute processing device for extracting attributes associated with COI from natural language sentences, the COI attribute processing device comprising:
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a processor; and a memory communicatively coupled to the processor, wherein the memory stores processor instructions, which, on execution, causes the processor to; create an input vector comprising a plurality of parameters for each target word in a sentence inputted by a user, wherein the plurality of parameters for each target word comprise a Part of Speech (POS) vector associated with the target word and at least two words preceding the target word, a word embedding for the target word, a word embedding for a head word of the target word in a dependency parse tree for the sentence, and a dependency label for the target word; process for each target word, the input vector through a trained bidirectional Gated Recurrent Unit (GRU) neural network, wherein the trained bidirectional GRU neural network is trained to identify attributes associated with COI from a plurality of sentences, and wherein attributes associated with a COI in a sentence augment the context of the COI; associate COI attribute tags to each target word in the sentence based on processing of associated input vector through the trained bidirectional GRU neural network; extract attributes from the sentence based on the COI attribute tags associated with each target word in the sentence; and provide a response to the sentence inputted by the user based on the attributes extracted from the sentence. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A non-transitory computer-readable storage medium having stored thereon, a set of computer-executable instructions causing a computer comprising one or more processors to perform steps comprising:
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creating an input vector comprising a plurality of parameters for each target word in a sentence inputted by a user, wherein the plurality of parameters for each target word comprise a Part of Speech (POS) vector associated with the target word and at least two words preceding the target word, a word embedding for the target word, a word embedding for a head word of the target word in a dependency parse tree for the sentence, and a dependency label for the target word; processing for each target word, the input vector through a trained bidirectional Gated Recurrent Unit (GRU) neural network, wherein the trained bidirectional GRU neural network is trained to identify attributes associated with COI from a plurality of sentences, and wherein attributes associated with a COI in a sentence augment the context of the COI; associating COI attribute tags to each target word in the sentence based on processing of associated input vector through the trained bidirectional GRU neural network; extracting attributes from the sentence based on the COI attribute tags associated with each target word in the sentence; and providing a response to the sentence inputted by the user based on the attributes extracted from the sentence.
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Specification