Method and system for identifying user intent from user statements

A user intent determination system for identifying user intent from user statements, 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 an input statement provided by a user from a Natural Language Understanding (NLU) engine, wherein the input statement is processed to remove one or more irrelevant content;
• extract a plurality of features for each word in the processed input statement, wherein the plurality of features comprises a Parts of Speech (POS) label, a dependency parse tree and word embeddings;
• predict a class for each word in the processed input statement from a plurality of predefined classes using a neural network model, wherein the neural network model predicts the class for each word based on an input vector generated for each word based on the plurality of features, wherein the input vector comprises the POS label for a target word selected from a plurality of words present in the input statement, the POS label of a predetermined number of words prior to the target word, word embeddings of the target word, a word embedding of a head word in the dependency parse tree and a dependency label for the target word;
• and
• identify the user intent based on the class predicted for each word in the processed input statement, wherein the user intent is provided to the NLU engine to provide a response to the input statement based on the user intent.

A non-transitory computer readable medium including instruction stored thereon that when processed by at least one processor cause a user intent determination system to perform operations comprising:

• receiving an input statement provided by a user from a Natural Language Understanding (NLU) engine, wherein the input statement is processed to remove one or more irrelevant content;
• extracting a plurality of features for each word in the processed input statement, wherein the plurality of features comprises a Parts of Speech (POS) label, a dependency parse tree and word embeddings;
• predicting a class for the each word in the processed input statement from a plurality of predefined classes using a neural network model, wherein the neural network model predicts the class for the each word based on an input vector generated for the each word based on the plurality of features, wherein the input vector comprises the POS label for a target word selected from a plurality of words present in the input statement, the POS label of a predetermined number of words prior to the target word, word embeddings of the target word, a word embedding of a head word in the dependency parse tree and a dependency label for the target word;
• and
• identifying the user intent based on the class predicted for the each word in the processed input statement, wherein the user intent is provided to the NLU engine to provide a response to the input statement based on the user intent.