Deep Learning-based Text-Retrieval System with Relevance Feedback
- By Simran Kalra1
-
View Affiliations Hide Affiliations1 Centre for Interdisciplinary Research in Business and Technology, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
- Source: Demystifying Emerging Trends in Machine Learning , pp 171-180
- Publication Date: February 2025
- Language: English
Deep Learning-based Text-Retrieval System with Relevance Feedback, Page 1 of 1
< Previous page | Next page > /docserver/preview/fulltext/9789815305395/chapter-16-1.gif
We presented an Information Retrieval (IR) system that learns from existing information and creates a single pertinent document that, we presume, has all its indexed pertinent details for a query. Deep learning makes such a system viable. We then asked people to score the query plus word-cloud representation of three randomly selected relevant texts and our new synthetic document. The synthetic document topped all inquiries and users. We then trained a CNN using query-relevant data. We performed "deep learn" function on a synthetic, relevant material using the CNN. We used crowdsourcing to compare the "deep-learned" material to related documents. Users can see a query and four-word cloud (three relevant documents and our deep learning synthetic document). The synthetic document provides the the most relevant feedback.
-
From This Site
/content/books/9789815305395.chapter-16dcterms_subject,pub_keyword-contentType:Journal -contentType:Figure -contentType:Table -contentType:SupplementaryData105
