Abstract: The disclosure relates to a method for receiving labelled data from data sources; generating a plurality of segments from the labelled data using tokenizers, wherein each of the segments is associated with one or more classes, and wherein the plurality of segments comprises a sub-plurality of unique segments; calculating an entropy value for each of the sub-plurality of unique segments; for each class, generating one or more rules based on each of the segments associated with the class and the entropy value of each such segment; and combining the generated one or more rules with a model to improve prediction performance of the model. The labelled data is input data for a classifier model to form predictions and false positives are extracted from the predictions. The model explainability techniques and the false positives are used to create additional rule.

Abstract: A method for aspect-based sentiment analysis includes receiving a collection of textual data, extracting a set of aspects and a set of sentiment words from the textual data, identifying a set of aspect-sentiment word pairs from the extracted aspects and sentiment words, identifying a subset of aspect-sentiment word pairs according to a set of predefined rules, and grouping a plurality of aspects associated with the subset of aspect-sentiment word pairs into one or more clusters. Each of the set of aspect-sentiment word pairs includes an aspect word from the set of aspects and a sentiment word from the set of sentiment words. Each of the subset of aspect-sentiment word pairs is determined to have an aspect-sentiment relationship according to the set of predefined rules.

Abstract: A method and system are provided for training a machine-learning (ML) system/module and to provide an ML model. In one embodiment, a method includes using a labeled entities set to train a machine learning (ML) system, to obtain an ML model, and using the trained ML model to predict labels for entities in an unlabeled entities set, yielding a machine-labeled entities set. One or more individual ML models may be trained and used in this way, where each individual ML model corresponds to a respective document source. The document sources can be identified via classification of a corpus of documents. The prediction of labels provides a respective confidence score for each machine-labeled entity. The method also includes selecting from the machine-labeled entities set, a subset of machine-labeled entities having a respective confidence score at least equal to a threshold confidence score; and updating the labeled entities set by adding thereto the selected subset of machine-labeled entities.

Abstract: A method and system for generating and correcting chatbot responses based on reinforcement learning (RL) are disclosed. In some embodiments, the method includes receiving user data associated with a user in a chatbot conversation. The method includes providing a first recommendation to the user. The method includes detecting user feedback to the first recommendation in the chatbot conversation. The method then includes determining whether to assign a positive reward or a negative reward to the user feedback based on sentiment analysis performed on the user feedback. If the negative reward is assigned to the user feedback, the method further includes calculating a negative reward score for the first recommendation; retraining the one or more of RL models using one or more of the negative reward score, the user data, the first recommendation, and the user feedback; and determining a second recommendation using the one or more retrained RL models.

Abstract: A method and system for handling unlabeled interaction data with contextual understanding are disclosed. In some embodiments, the method includes receiving the interaction data describing agent-consumer interactions associated with a contact center. The method includes analyzing the interaction data to identify a plurality of features. The method includes automatically performing taxonomy driven classification on the plurality of features to generate a first set of labels associated with the interaction data. The method includes training a deep learning model using the first set of labels and the interaction data to determine a second set of labels. The method then includes intelligently combining the first and second sets of labels to obtain a combined set of labels associated with the interaction data.

Abstract: A method and system for generating and correcting chatbot responses based on reinforcement learning (RL) are disclosed. In some embodiments, the method includes receiving user data associated with a user in a chatbot conversation. The method includes providing a first recommendation to the user. The method includes detecting user feedback to the first recommendation in the chatbot conversation. The method then includes determining whether to assign a positive reward or a negative reward to the user feedback based on sentiment analysis performed on the user feedback. If the negative reward is assigned to the user feedback, the method further includes calculating a negative reward score for the first recommendation; retraining the one or more of RL models using one or more of the negative reward score, the user data, the first recommendation, and the user feedback; and determining a second recommendation using the one or more retrained RL models.

Abstract: A method and a system are provided for training a machine-learning (ML) system to function as a chatbot. According to one embodiment, a method for training and ML system includes providing to the machine-learning system: in a first iteration, a first input-output pair that includes a first input and a first output; and, in a second iteration, a second input-output pair that includes a second input and a second output, where the second input includes the first input-output pair and the second output is different from the first output, so that a context for the second input-output pair is stored in the memory of the ML system.

Abstract: A method and system are provided for training a machine-learning (ML) system/module and to provide an ML model. In one embodiment, a method includes using a labeled entities set to train a machine learning (ML) system, to obtain an ML model, and using the trained ML model to predict labels for entities in an unlabeled entities set, yielding a machine-labeled entities set. One or more individual ML models may be trained and used in this way, where each individual ML model corresponds to a respective document source. The document sources can be identified via classification of a corpus of documents. The prediction of labels provides a respective confidence score for each machine-labeled entity. The method also includes selecting from the machine-labeled entities set, a subset of machine-labeled entities having a respective confidence score at least equal to a threshold confidence score; and updating the labeled entities set by adding thereto the selected subset of machine-labeled entities.

Abstract: A method and a system are provided for training a machine-learning (ML) system to function as a chatbot. According to one embodiment, a method for training and ML system includes providing to the machine-learning system: in a first iteration, a first input-output pair that includes a first input and a first output; and, in a second iteration, a second input-output pair that includes a second input and a second output, where the second input includes the first input-output pair and the second output is different from the first output, so that a context for the second input-output pair is stored in the memory of the ML system.