Abstract: Sustainability measurement is critical to determine whether industry performance is heading in intended direction. State of the art systems in the field of sustainability measurement fail to consider many parameters which are indicative of the sustainability of industries. The disclosure herein generally relates to industry monitoring, and, more particularly, to a method and system for sustainability measurement in an industrial environment. The system calculates similarity score which indicates similarity between different sentences and indicators, and used the calculated similarity scores and extracted features to classify the sentences as belonging to specific classes. This information is in turn used for measuring sustainability of organization from which input data have been collected.

Abstract: With the proliferation of data and documents available on the internet and other information sources, analysis of adverse events poses a serious technical challenge on account of associated data volume and variety. This disclosure relates generally to identification and profiling of adverse events. By receiving a set of articles from a plurality of data sources and utilizing a series of Natural Language Processors, NLP techniques are employed to identify implicit and explicit adverse events. Entity statistics and sentiment extraction and analysis is performed. An ontology based adverse event identification framework is proposed for identification and profiling of implicit adverse event. An attention based bi-directional long short term memory network for adverse event identification and classification is proposed.

Abstract: Conventionally, text summarization has been rule-based method and neural network based which required large dataset for training and the summary delivered had to be assessed by user in terms of relevancy. System and method are provided by present disclosure that generate causal insight summaries wherein event of importance is detected, and it is determined why event is relevant to a user. Text description is processed for named entities recognition, polarities of sentences identified, extraction of causal effects sentences (CES) and causal relationship identification in text segments which correspond to impacting events. Named entities are then role labeled. A score is computed for named entities, polarities of sentences, causal effects sentences, causal relationships, and the impacting events. A causal insight summary is generated with overall polarity being computed/determined.

Abstract: Sustainability measurement is critical to determine whether industry performance is heading in intended direction. State of the art systems in the field of sustainability measurement fail to consider many parameters which are indicative of the sustainability of industries. The disclosure herein generally relates to industry monitoring, and, more particularly, to a method and system for sustainability measurement in an industrial environment. The system calculates similarity score which indicates similarity between different sentences and indicators, and used the calculated similarity scores and extracted features to classify the sentences as belonging to specific classes. This information is in turn used for measuring sustainability of organization from which input data have been collected.

Abstract: With the proliferation of data and documents available on the internet and other information sources, analysis of adverse events poses a serious technical challenge on account of associated data volume and variety. This disclosure relates generally to identification and profiling of adverse events. By receiving a set of articles from a plurality of data sources and utilizing a series of Natural Language Processors, NLP techniques are employed to identify implicit and explicit adverse events. Entity statistics and sentiment extraction and analysis is performed. An ontology based adverse event identification framework is proposed for identification and profiling of implicit adverse event. An attention based bi-directional long short term memory network for adverse event identification and classification is proposed.

Abstract: The present subject matter relates to analysis of time-series data based on world events derived from unstructured content. According to one embodiment, a method comprises obtaining event information corresponding to at least one world event from unstructured content obtained from a plurality of data sources. The event information includes at least time of occurrence of the world event, time of termination of the world event, and at least one entity associated with the world event. Further, the method comprises retrieving time-series data pertaining to the entity associated with the world event from a time-series data repository. Based on the event information and the time-series data, the world event is aligned and correlated with at least one time-series event to identify at least one pattern indicative of cause-effect relationship amongst the world event and the time-series event.

Abstract: The present subject matter relates to analysis of time-series data based on world events derived from unstructured content. According to one embodiment, a method comprises obtaining event information corresponding to at least one world event from unstructured content obtained from a plurality of data sources. The event information includes at least time of occurrence of the world event, time of termination of the world event, and at least one entity associated with the world event. Further, the method comprises retrieving time-series data pertaining to the entity associated with the world event from a time-series data repository. Based on the event information and the time-series data, the world event is aligned and correlated with at least one time-series event to identify at least one pattern indicative of cause-effect relationship amongst the world event and the time-series event.