Feedback-Based Keyphrase Extraction from Unstructured Text Documents

TLDR: This paper brings a human in the loop, and enable a human teacher to give feedback to a key-tags extraction framework in the form of natural language, in which the quality of the output can easily be judged by non-experts.

Abstract: Machine Learning experts use classification and tagging algorithms considering the black box nature of these algorithms. These algorithms, primarily key-tags extraction from unstructured text documents are meant to capture key concepts in a document. With increasing amount of data, size and complexity of the data, this problem is key in industrial setup. Different possible use cases being in IT support, conversational systems/ chatbots and financial domains, this problem is important as shown in [1], [2]. In this paper, we bring a human in the loop, and enable a human teacher to give feedback to a key-tags extraction framework in the form of natural language. We focus on the problem of key-tags extraction in which the quality of the output can easily be judged by non-experts. Our system automatically reads natural language documents, extracts key concepts and presents an interactive information exploration user interface for analysing these documents.