Abstract: A method and device for extracting Point of Interest (POI) 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 LSTM neural network, which is trained to identify POI from a plurality of sentences. The method includes associating POI tags to each target word in the sentence based on processing of associated input vector through the trained bidirectional LSTM neural network. The method further includes extracting POI text from the sentence based on the POI 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 POI text extracted from the sentence.

Abstract: A method and device for extracting Action of Interest (AOI) 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 neural network with RELU activation, which is trained to identify AOI from a plurality of sentences. The method includes assigning AOI tags to each target word in the sentence based on processing of associated input vector through the trained neural network with RELU activation. The method further includes extracting AOI text from the sentence based on the AOI tags assigned to each target word in the sentence. The method further includes providing a response to the sentence inputted by the user based on the AOI text extracted from the sentence.

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.

Abstract: A method and system for generating theme based summary from unstructured content is disclosed. The method includes assigning a sentiment category of a plurality of sentiment categories to each of a plurality of sets of words. The method further includes segregating the plurality of sets of words based on the assigned sentiment category. The method may further include processing for each of the plurality of sets of words each word in a set of words of the plurality of sets of words as a neuron in the first neural network. The method may further include determining for each of the plurality of sets of words a relevancy score for each neuron relative to an associated sentiment category. The method may further include generating a summary from the unstructured text, based on the relevancy score determined for each neuron.

Abstract: In some aspects, the present disclosure provides compounds of the formula: (I) or (II) wherein the variables are as defined herein. In some embodiments, these compounds may be used to treat cancer or other hyperproliferative diseases, as well as atherosclerosis and coronary artery disease.

Abstract: A method an system for extracting factoid associated words from natural language sentences is disclosed. The method includes creating an input vector that includes a plurality of parameters for each target word in a sentence. For a target word, the plurality of parameters includes a Part of Speech (POS) vector, a word embedding, a word embedding for a head word of the target word, a dependency label, and a semantic role label. The method includes processing for each target word, the input vector through a trained neural network and assigning one or more factoid tags to each target word in the sentence. The method includes extracting text associated with factoids from the sentence based on the one or more factoid tags. The method further includes providing a response to the sentence inputted by the user based on the text associated with the factoids.

Abstract: The present disclosure relates to a method and a system for generating sentiment-based summaries for a user review. In an embodiment, a text analyzer receives a block of text indicating a user review. The text analyzer may generate one or more vectors for the plurality of words. Further, a relation is identified among the one or more vectors. A model is trained to identify a relation among the one or more vectors. Using the relation between the one or more vectors, a sentiment associated with the block of text is determined. Thereafter, one or more keywords from the block of text contributing to the determined sentiment is are identified and are classified into categories according to the sentiment contributed by the one or more words. Thereafter, the summary is generated for each category using the corresponding one or more words.

Abstract: The present disclosure discloses method and a user intent identification system for identifying user intent from user statements. The user intent identification system receives input statement provided by a user from a Natural Language Understanding (NLU) engine. The input statement is processed to remove one or more irrelevant content. A plurality of features for each word in the processed input statement is extracted. The plurality of features comprises Parts of Speech (POS) label, dependency parse tree and word embeddings. The user intent determination system predicts class for each word in the processed input statement from a plurality of predefined classes using a neural network model. The neural network model predicts class for each word based on input vector generated for the each word based on the plurality of features. Thereafter, the user intent is identified based on class predicted for each word in processed input statement.

Abstract: This disclosure relates to method and system for providing an explanation for a prediction generated by an artificial neural network (ANN) model for a given input data. The method may include receiving the given input data and the prediction generated by the ANN model. The ANN model may be built and trained for a target application. The method may further include determining a plurality of relevant portions of the given input data. For each of the plurality of relevant portions, the method may further include fetching a portional prediction and a portional prediction score generated by the ANN model, and determining a degree of influence score based on the portional prediction score and a comparison between the portional prediction and the prediction. The method may further include providing the explanation for the prediction based on the degree of influence score of each of the plurality of relevant portions.

Abstract: Disclosed herein is method and system for identifying one or more Places of Interest (PoI) in a natural language system. Word embedding representation for each word in the natural language input are retrieved from a knowledge repository. Further, for each word, Part-of-Speech (POS) tags are tagged, and dependency labels are generated. Subsequently, a PoI tag is assigned to each word based on the word embedding representation, the POS and the dependency labels of each word. Finally, the one or more PoI are identified based on PoI tag assigned to each word. In an embodiment, the method of present disclosure helps in dynamically identifying one or more PoI from natural language text utterances in interactive systems, thereby enhancing usability of interaction based intelligent systems.

Abstract: A method and device for extracting causal from natural language sentences is disclosed. The method includes determining, by a computing device, a plurality of parameters for each target word in a sentence inputted by a user. The method further includes processing for each target word, by the computing device, an input vector comprising the plurality of parameters for a causal classifier neural network. The method includes identifying, by the computing device, causal tags associated with each target word in the sentence based on processing of associated input vector. The method includes extracting, by the computing device, the causal text from the sentence based on the causal tags associated with each target word in the sentence. The method further includes providing, by the computing device, a response to the sentence inputted by the user based on the causal text extracted for the sentence.

Abstract: A method and device for extracting Point of Interest (POI) 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 LSTM neural network, which is trained to identify POI from a plurality of sentences. The method includes associating POI tags to each target word in the sentence based on processing of associated input vector through the trained bidirectional LSTM neural network. The method further includes extracting POI text from the sentence based on the POI 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 POI text extracted from the sentence.

Abstract: A method and device for extracting Action of Interest (AOI) 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 neural network with RELU activation, which is trained to identify AOI from a plurality of sentences. The method includes assigning AOI tags to each target word in the sentence based on processing of associated input vector through the trained neural network with RELU activation. The method further includes extracting AOI text from the sentence based on the AOI tags assigned to each target word in the sentence. The method further includes providing a response to the sentence inputted by the user based on the AOI text extracted from the sentence.

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.

Abstract: A method and device for extracting causal from natural language sentences is disclosed. The method includes determining, by a computing device, a plurality of parameters for each target word in a sentence inputted by a user. The method further includes processing for each target word, by the computing device, an input vector comprising the plurality of parameters for a causal classifier neural network. The method includes identifying, by the computing device, causal tags associated with each target word in the sentence based on processing of associated input vector. The method includes extracting, by the computing device, the causal text from the sentence based on the causal tags associated with each target word in the sentence. The method further includes providing, by the computing device, a response to the sentence inputted by the user based on the causal text extracted for the sentence.

Abstract: Disclosed herein is method and system for identifying one or more Places of Interest (PoI) in a natural language system. Word embedding representation for each word in the natural language input are retrieved from a knowledge repository. Further, for each word, Part-of-Speech (POS) tags are tagged, and dependency labels are generated. Subsequently, a PoI tag is assigned to each word based on the word embedding representation, the POS and the dependency labels of each word. Finally, the one or more PoI are identified based on PoI tag assigned to each word. In an embodiment, the method of present disclosure helps in dynamically identifying one or more PoI from natural language text utterances in interactive systems, thereby enhancing usability of interaction based intelligent systems.

Abstract: A system may read an input file having an input file size and including a first record and a second record. The first and second record may each have a record length. The system may parse the input file into a first split file and a second split file, with the first split file including the first record and the second split file including the second record. The system may distribute the first split file to a first node to generate a first output file and the second split file to a second node to generate a second output file. Any number of additional split files may be distributed to generate any number output files. The system may combine the output files to generate a converted data file.

Abstract: A method for floating node reduction uses a capacitance matrix that specifies coupling capacitances between signal nodes and floating nodes of an interconnect structure. Random walks are performed from a first signal node to the other signal nodes, wherein each of the random walks traverses one or more of the floating nodes. Each of the random walks is directed based on probabilities derived from the coupling capacitances of the capacitance matrix. A count is maintained for each of the other signal nodes, wherein each count specifies a number of the random walks that end on the corresponding signal node. The indirect coupling capacitance from the first signal node to a second signal node is selected to correspond with the total indirect coupling capacitance of the first signal node, times the count associated with the second signal node, divided by the total number of random walks.

Abstract: Systems and methods for determining action items from knowledge base for execution of operation. The system receives instructions, present in a knowledge base, which are required to execute one or more operations. Thereafter, the system parses the instructions into one or more sentences and assigns a POS tag for each word in the one or more sentences. Further, the system assigns a predefined class for each of the POS tagged word. Based on the predefined class, the system determines the action items. The action item comprises one or more actions and one or more components on which the one or more actions are to be performed. The present disclosure enables automated systems to easily execute one or more operation based on the action items thereby reducing the delay in performance of the automated system due to complexity in interpreting the instructions.

Abstract: The method includes receiving a data file comprising a record; identifying a characteristic of the record; appending a characteristic marker to the record reflecting the characteristic; receiving a data request from a user; identifying a clearance identifier associated with the user, wherein the clearance identifier indicates whether the user has clearance to access the record based on the characteristic of the record; retrieving the record in response to the receiving the data request; comparing the characteristic marker of the record with the clearance identifier; and/or determining whether the user has clearance to access the record.