Claims:We Claim:
1. A method for generating a plurality of structured relations between a plurality of words in a plurality of sentences, the method comprising:
receiving, by a processor, a plurality of sentences comprising a plurality of words, wherein the plurality of sentences comprises numerical data and textual data;
generating, by the processor, a plurality of encoded hidden state vectors associated with the plurality of sentences using a single layer bi-directional Long Short Term Memory (LSTM) neural network;
generating, by the processor, a plurality of current hidden state vectors based on word embedding associated with each word in the plurality of sentences at a time stamp ‘t’;
computing, by the processor, an attention distribution of each word in the plurality of sentences based on the plurality of encoded hidden state vectors and plurality of current hidden state vectors, wherein the attention distribution is indicative of importance of each word in the plurality of sentences;
computing, by the processor, a context vector of the plurality of sentences based on the attention distribution of each word in the plurality of sentences and the plurality of encoded hidden state vectors;
computing, by the processor, a vocabulary distribution at the time stamp “t” based on the context vector and the plurality of current hidden state vectors;
computing, by the processor, a probability distribution of each of the plurality of words in the plurality of sentences based on the plurality of encoded hidden state vectors, the plurality of current hidden state vectors, and the vocabulary distribution;
generating, by the processor, an output comprising a plurality of structured relations between the plurality of words based on the probability distribution; and
rendering, by the processor, a knowledge graph depicting the plurality of structured relations between the plurality of words.
2. The method of claim 1, further comprising computing a coverage vector based on the probability distribution and the attention distribution, wherein the coverage vector avoids duplicate generation of relations within the generated plurality of structured relations.
3. The method of claim 1, further comprising selecting one of: generation of structured relations and sampling of the plurality of words from the plurality of sentences based on the probability distribution.
4. The method of claim 1, wherein the probability distribution includes a pointer mechanism to control when to generate a new relation or to copy the received words to the output, wherein the probability distribution is further indicative of a location of a word in the plurality of sentences, and wherein based on the pointer mechanism words in the plurality of sentences are directly copied from the received plurality of sentences to the output, and wherein the output is independent of an output length.
5. The method of claim 1, wherein the plurality of current hidden state vectors is used to generate a plurality of output words that is indicative of mapping of the plurality of words in the plurality of sentence.
6. The method of claim 1, wherein the context vector is a weighted sum between the attention distribution and the plurality of encoded hidden state vectors.
7. The method of claim 1, wherein the context vector is further concatenated with the plurality of current hidden state vectors at the time stamp “t”.
8. The method of claim 1, wherein the plurality of words in the plurality of sentences correspond to plurality of annotated entities.
9. An application server to generate a plurality of structured relations between a plurality of words in a plurality of sentences, the application server comprising:
a processor; and
a memory communicatively coupled to the processor, wherein the memory stores processor instructions, which, on execution, causes the processor to:
receive a plurality of sentences comprising a plurality of words, wherein the plurality of sentences comprises numerical data and textual data;
generate a plurality of encoded hidden state vectors associated with the plurality of sentences using a single layer bi-directional Long Short Term Memory (LSTM) neural network;
generate a plurality of current hidden state vectors based on word embedding associated with each word in the plurality of sentences at a time stamp ‘t’;
compute an attention distribution of each word in the plurality of sentences based on the plurality of encoded hidden state vectors and plurality of current hidden state vectors, wherein the attention distribution is indicative of importance of each word in the plurality of sentences;
compute a context vector of the plurality of sentences based on the attention distribution of each word in the plurality of sentences and the plurality of encoded hidden state vectors;
compute a vocabulary distribution at the time stamp “t” based on the context vector and the plurality of current hidden state vectors;
compute a probability distribution of each of the plurality of words in the plurality of sentences based on the plurality of encoded hidden state vectors, the plurality of current hidden state vectors, and the vocabulary distribution;
generate an output comprising a plurality of structured relations between the plurality of words based on the probability distribution; and
render a knowledge graph depicting the plurality of structured relations between the plurality of words.
10. The application server of claim 9, wherein the processor is further configured to:
compute a coverage vector based on the probability distribution and the attention distribution, wherein the coverage vector avoids duplicate generation of relations within the generated plurality of structured relations, and
select one of: generation of structured relations and sampling of the plurality of words from the plurality of sentences based on the probability distribution.
Dated this 8th day of March, 2019

Madhusudan S T
Of K&S Partners
Agent for the Applicant
IN/PA-1297 , Description:Technical Field
The present subject matter is related, in general to natural language processing, and more particularly, but not exclusively to a method and system for generating a plurality of structured relations between a plurality of words in a plurality of sentences.