Abstract: The present disclosure discloses method and system for extracting relevant entities from a text corpus. The method comprises receiving, by the entity extraction computing device, a text corpus and an entity, determining at least one feature for each block of text from the text corpus, where the at least one feature corresponds to predefined one or more feature heads, calculating a score for each block of text from the text corpus based on training of the entity extraction system, determining a template from one or more templates based on the score, where the one or more templates are generated based on the training of the entity extraction system, and extracting at least one relevant entity from the text corpus, with respect to the entity, based on the template. The method and system disclosed in the present disclosure may be used to extract relevant entities across various domains by training the system.

Abstract: Disclosed herein is method and system for handling issues in a computing environment. An issue template is generated by mapping problem statements of the issues with system log information of the computing environment. Critical features in the issue templates are determined based on natural language analysis of the issue templates and predetermined parameters associated with the issues. Further, one or more clusters of issues are created based on semantic similarity analysis of the critical features, and finally, a correlation map of various clusters is created to dynamically identify issue resolution scripts required for handling the issues. The present method facilitates automated mapping of an issue to corresponding problem statements and action scripts, thereby eliminating manual intervention associated with classifying and handling of the issues in the computing environment.

Abstract: The present disclosure discloses method and system for extracting relevant entities from a text corpus. The method comprises receiving, by the entity extraction computing device, a text corpus and an entity, determining at least one feature for each block of text from the text corpus, where the at least one feature corresponds to predefined one or more feature heads, calculating a score for each block of text from the text corpus based on training of the entity extraction system, determining a template from one or more templates based on the score, where the one or more templates are generated based on the training of the entity extraction system, and extracting at least one relevant entity from the text corpus, with respect to the entity, based on the template. The method and system disclosed in the present disclosure may be used to extract relevant entities across various domains by training the system.

Abstract: A method and a system are described for summarizing a conversation. The method includes splitting a plurality of sentences in the conversation into one or more phrases based on pre-defined grammar rules. The method includes extracting one or more relevant phrases associated with each of the one or more phrases based on domain dictionaries and pre-defined knowledge base. The method includes determining a correlation between each of the one or more phrases and the one or more relevant phrases based on a relationship score. The method includes identifying at least one of a core topic of discussion in the conversation, one or more issues, one or more resolved issues, and one or more unresolved issues based on the correlation. The method includes creating a summary of the conversation based on the core topic of discussion, one or more issues, one or more resolved issues, and one or more unresolved issues.