Feedback-Based Keyphrase Extraction from Unstructured Text Documents

Nishtha Madaan, Mudit Saxena, Hima Patel, S. Mehta
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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.
基于反馈的非结构化文本关键字提取
机器学习专家使用分类和标记算法,考虑到这些算法的黑箱性质。这些算法(主要是从非结构化文本文档中提取密钥标记)旨在捕获文档中的关键概念。随着数据量、大小和复杂性的增加,这个问题在工业设置中是关键。在IT支持、会话系统/聊天机器人和金融领域的不同可能用例中,这个问题很重要,如[1]、[2]所示。在本文中,我们将人类引入到循环中,并使人类教师能够以自然语言的形式向关键标签提取框架提供反馈。我们重点研究了关键标签的提取问题,其中输出的质量很容易被非专家判断。我们的系统自动读取自然语言文档,提取关键概念,并提供一个交互式信息探索用户界面来分析这些文档。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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