Nishtha Madaan, Mudit Saxena, Hima Patel, S. Mehta
{"title":"Feedback-Based Keyphrase Extraction from Unstructured Text Documents","authors":"Nishtha Madaan, Mudit Saxena, Hima Patel, S. Mehta","doi":"10.1109/COMSNETS48256.2020.9027399","DOIUrl":null,"url":null,"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.","PeriodicalId":265871,"journal":{"name":"2020 International Conference on COMmunication Systems & NETworkS (COMSNETS)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on COMmunication Systems & NETworkS (COMSNETS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMSNETS48256.2020.9027399","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.