Abstract: The present disclosure includes systems, apparatuses, and methods for event identification. In some aspects, a method includes receiving data including text and performing natural language processing on the received data to generate processed data that indicates one or more sentences. The method also includes generating, based on a first keyword set, a second keyword set having more keywords than the first keyword set. The method further includes, for each of the first and second keyword sets: detecting one or more keywords and one or more entities included in the processed data, determining one or more matched pairs based on the detected keywords and entities, and extracting a sentence, such as a single sentence or multiple sentences, from a document based on the one or more sentences indicated by the processed data. The method may also include outputting at least one extracted sentence.

Abstract: The present disclosure includes systems, apparatuses, and methods for event identification. In some aspects, a method includes receiving data including text and performing natural language processing on the received data to generate processed data that indicates one or more sentences. The method also includes generating, based on a first keyword set, a second keyword set having more keywords than the first keyword set. The method further includes, for each of the first and second keyword sets: detecting one or more keywords and one or more entities included in the processed data, determining one or more matched pairs based on the detected keywords and entities, and extracting a sentence, such as a single sentence or multiple sentences, from a document based on the one or more sentences indicated by the processed data. The method may also include outputting at least one extracted sentence.

Abstract: Aspects of the present disclosure provide systems, methods, apparatus, and computer-readable storage media for determining semantic relationships between hashtags in social media messages, particularly for use in generating a hierarchical ontology of hashtags based on co-occurrence frequency and diversity metrics. For example, natural language processing may be performed on a plurality of social media messages to extract hashtags from the social media messages. Co-occurrence frequency counts for at least two hashtags and other hashtags, in addition to an ensemble score based on a combination of one or more diversity metrics, may be determined. A hierarchical ontology may be generated based on the co-occurrence frequency counts and the ensemble scores for the at least two hashtags. Such a hierarchical ontology may group hashtags into communities of common topics that are ordered based on ensemble scores.