Abstract: This disclosure relates generally to method and system to classify news snippets into categories using an ensemble of machine learning models. The ensemble is between a bidirectional long short memory (BILSTM) based text classification network and a pretrained language model (PLM) based natural language inference (NLI) which is robust and accurate for such categorization. The method trains a first machine learning model using a training dataset to learn text representations. Further, the training dataset is used to finetune a second machine learning model to classify at least one unlabeled news snippet of unknown category based on a premise-hypothesis pair. Further, an ensemble of machine learning models is generated by using the first machine learning model and the second machine learning model to classify a set of test news snippets received as input request to corresponding category.

Abstract: This disclosure relates to the field of incident analysis, and, more particularly, to systems and methods for similarity analysis in incident reports using event timeline representations. Conventionally, processing of repositories of incident reports to identify similar incidents is challenging due to use of unstructured text data in describing the incident reports. Timeline representation is an important knowledge representation which captures chronological ordering of the events. The timeline representation becomes useful in process of root cause analysis as causes would temporally precede the effect. To construct event timeline representations, chronological ordering of events is required. The present disclosure provides a temporal relation identification technique to obtain a timeline representation of the events. Further, a similarity identification approach is used that makes use of neural embeddings to identify similar timeline representations and in turn, similar incident reports.

Abstract: Narrative texts contain rich knowledge about actors and interactions among them. It is often useful to extract and visualize these interactions through a set of inter-related timelines in which an actor has participated. Current approaches utilize labeled datasets and implement supervised techniques and thus are not suitable. Embodiments of the present disclosure implement systems and methods for automated extraction of Message Sequence Chart (MSC) from textual description by identifying verbs which indicate interactions and then use dependency parsing and Semantic Role Labelling based approaches to identify senders (initiating actors) and receivers (other actors involved) for these interaction verbs. The present disclosure further employs an optimization-based approach to temporally re-order these interactions.

Abstract: System and method for automatic entity relationship (ER) model generation for services as software is disclosed. ER model generation by automated knowledge acquisition is disclosed, and automation of knowledge generation process is disclosed. Information extraction process is automated and multilevel validation of information extraction process is provided. System comprises training module to train information extraction model, and knowledge generation module for population of ER model. Standard Operators are generated based on the ER model so generated (populated). Context aware entity extraction is implemented for the ER model generation. System and method leverages existing ER model to make the system self-learning and intelligent, and provides common platform for knowledge generation from different data sources comprising documents, database, website, web corpus, and blog.

Abstract: Narrative texts contain rich knowledge about actors and interactions among them. It is often useful to extract and visualize these interactions through a set of inter-related timelines in which an actor has participated. Current approaches utilize labeled datasets and implement supervised techniques and thus are not suitable. Embodiments of the present disclosure implement systems and methods for automated extraction of Message Sequence Chart (MSC) from textual description by identifying verbs which indicate interactions and then use dependency parsing and Semantic Role Labelling based approaches to identify senders (initiating actors) and receivers (other actors involved) for these interaction verbs. The present disclosure further employs an optimization-based approach to temporally re-order these interactions.

Abstract: A recruiting person handling profiles of candidates need thorough knowledge about various technologies related to requirements posted with respect to a job opening, so as to correctly interpret and identify skill level of each candidate. Lack of knowledge of the recruiting person may result in skilled candidates not getting shortlisted and candidates having no or less relevant skills getting selected, which would affect work force of an organization the candidates are being recruited for. The disclosure herein generally relates to data processing, and, more particularly, to a method and a system for determining skill similarity by using the data processing. The system automatically identifies skills that match each other, and the recruiting person may use this information to identify and shortlist right candidates for the job. The system generates skill vectors for each skill, and by comparing skill vectors of different skills, identifies skills that are similar to each other.

Abstract: System and method for automatic entity relationship (ER) model generation for services as software is disclosed. ER model generation by automated knowledge acquisition is disclosed, and automation of knowledge generation process is disclosed. Information extraction process is automated and multilevel validation of information extraction process is provided. System comprises training module to train information extraction model, and knowledge generation module for population of ER model. Standard Operators are generated based on the ER model so generated (populated). Context aware entity extraction is implemented for the ER model generation. System and method leverages existing ER model to make the system self-learning and intelligent, and provides common platform for knowledge generation from different data sources comprising documents, database, website, web corpus, and blog.