Advanced text tagging using key phrase extraction and key phrase generation

Abstract

The systems and methods described herein describe a comprehensive knowledge and / or skills management technology tool to address these and other issues with an advanced text tagging algorithm to extract the relevant topics from a text segment. The tagging algorithm includes a key phrase extraction technique and a key phrase generation technique. The key phrase extraction includes identifying phrases from the original text that represents its most relevant information. The key phrase generation technique includes generating additional phrases that do not necessarily appear in the text, but which describe its subject.

Description

CROSS-REFERENCE TO RELATED APPLICATION[0001]This application is a continuation of pending U.S. patent application Ser. No. 16 / 910,891, filed Jun. 24, 2020, entitled “ADVANCED TEXT TAGGING USING KEY PHRASE EXTRACTION AND KEY PHRASE GENERATION”, which is hereby incorporated herein by reference in its entirety.FIELD OF THE INVENTION[0002]The systems and methods described herein relate to machine learning-based approaches for resolving user queries via a dynamic determination and allocation of expert resources with an advanced text tagging algorithm that processes a text segment using key phrase extraction and key phrase generation.BACKGROUND OF THE INVENTION[0003]Conventional approaches to mapping and identifying expert resources suffers from one or more drawbacks. For example, in large organizations, the number of users and volume of content can make mapping and identifying expert resources problematic. Models of an organization's expert resources may be unable to factor in all of the...

AI Technical Summary

Benefits of technology

The invention provides a system that uses cognitive engines and predictive analytics to effectively and efficiently resolve user queries. The system uses machine learning to create a know-how map, linking users with their areas of expertise. Users are identified as expert users based on the tags associated with the question and their scores related to those tags. The system may also take into account which tags are already used in a particular instance and if a new tag is needed. The main technical effects include efficient and accurate routeing of questions to experts and maximizing the likelihood of getting a quality solution or answer to the question as soon as possible.

Problems solved by technology

The system may use unsupervised learning techniques, which are automated and require no human effort.

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