Claims:WE CLAIM
1. A method for extracting Action of Interest (AOI) from natural language sentences, the method comprising:
creating, by a AOI 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 the dependency parse tree of the sentence, and a dependency label for the target word;
processing for each target word, by the AOI processing device, the input vector through a trained neural network with Rectified Linear Units (RELU) activation, wherein the trained neural network with RELU activation is trained to identify words associated with AOI from a plurality of sentences;
assigning, by the AOI processing device, AOI tags to each target word in the sentence based on processing of associated input vector through the trained neural network with RELU activation;
extracting, by the AOI processing device, AOI text from the sentence based on the AOI tags associated with each target word in the sentence; and
providing, by the AOI processing device, a response to the sentence inputted by the user based on the AOI text extracted from the sentence.

2. The method of claim 1 further comprising determining the plurality of parameters for each target word in the sentence inputted by the user.

3. The method of claim 1, wherein the response comprises at least one of an answer to the query and an action corresponding to the query.

4. The method of claim 1, wherein the dependency label for the target word indicates relation of the target word with the head word in the sentence.

5. The method of claim 1 further comprising training the neural network with RELU activation to identify AOI tags for words within sentences.

6. The method of claim 5, wherein training the neural network with RELU activation comprises:
assigning AOI tags to each word in a plurality natural language sentences retrieved from a data repository of natural language sentences comprising a plurality of AOI scenarios;
inputting, iteratively, the assigned AOI tag associated with each word in the plurality of natural language sentences to the RELU neural network for training.

7. The method of claim 1, wherein the AOI tags comprise Begin AOI tag, Inside AOI tag, and Others tag.

8. The method of claim 7, wherein the assigning the AOI tags to each target word comprises:
assigning a Begin AOI tag to a word in the sentence marking the beginning of the AOI text;
assigning an Inside AOI tag to each word in the AOI text succeeding the word marking the beginning of the AOI text; and
assigning an Others tag to each remaining word in the sentence.

9. An Action of Interest (AOI) processing device for extracting AOI from natural language sentences, the AOI processing device comprising:
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 the dependency parse tree of the sentence, and a dependency label for the target word;
process for each target word the input vector through a trained neural network with Rectified Linear Units (RELU) activation, wherein the trained neural network with RELU activation is trained to identify words associated with AOI from a plurality of sentences;
assign AOI tags to each target word in the sentence based on processing of associated input vector through the trained neural network with RELU activation;
extract AOI text from the sentence based on the AOI tags associated with each target word in the sentence; and
provide a response to the sentence inputted by the user based on the AOI text extracted from the sentence.

10. The AOI processing device of claim 9, wherein the processor instructions further cause the processor to determine the plurality of parameters for each target word in the sentence inputted by the user.

11. The AOI processing device of claim 9, wherein the response comprises at least one of an answer to the query and an action corresponding to the query.

12. The AOI processing device of claim 9, wherein the dependency label for the target word indicates relation of the target word with the head word in the sentence.

13. The AOI processing device of claim 9, wherein the processor instructions further cause the processor to train the neural network with RELU activation to identify AOI tags for words within sentences.

14. The AOI processing device of claim 13, wherein to train the neural network with RELU activation, the processor instructions further cause the processor to:
assign AOI tags to each word in a plurality natural language sentences retrieved from a data repository of natural language sentences comprising a plurality of AOI scenarios;
iteratively input the assigned AOI tag associated with each word in the plurality of natural language sentences to the RELU neural network for training.

15. The AOI processing device of claim 9, wherein the AOI tags comprise Begin AOI tag, Inside AOI tag, and Others tag.

16. The AOI processing device of claim 15, wherein to assign the AOI tags to each target word, the processor instructions further cause the processor to:
assign a Begin AOI tag to a word in the sentence marking the beginning of the AOI text;
assign an Inside AOI tag to each word in the AOI text succeeding the word marking the beginning of the AOI text; and
assign an Others tag to each remaining word in the sentence.

Dated this 30th day of June, 2018

Swetha SN
Of K&S Partners
Agent for the Applicant
IN/PA-2123
, Description:TECHNICAL FIELD
This disclosure relates generally to processing natural language sentences and more particularly to method and device for extracting action of interest from natural language sentences.