Extraction of Effective Textual and Semantic Features in Learning to Rank for Web Document Retrieval
Ranking algorithms, as the core of web search systems, are responsible for finding and ranking the most relevant documents to user information needs from the crawled and indexed corpus. With the ever-increasing amount of available training data, ranking technologies are moving towards using Machine...
Main Authors: | Mohaddeseh Mahjoob, Faezeh Ensan, Sanaz Keshvari, Parastoo Jafarzadeh, Mohammadamin keyvanzad |
---|---|
Format: | Article |
Language: | fas |
Published: |
Iranian Research Institute for Information and Technology
2021-07-01
|
Series: | Iranian Journal of Information Processing & Management |
Subjects: | |
Online Access: | http://jipm.irandoc.ac.ir/article-1-4361-en.html |
Similar Items
-
A Knowledge-Driven Multimedia Retrieval System Based on Semantics and Deep Features
by: Antonio Maria Rinaldi, et al.
Published: (2020-10-01) -
Deep Level Markov Chain Model for Semantic Document Retrieval
by: Linh Bui Khanh, et al.
Published: (2018-12-01) -
Feature Ranking for Text Classifiers
by: Makrehchi, Masoud
Published: (2007) -
Feature Ranking for Text Classifiers
by: Makrehchi, Masoud
Published: (2007) -
Deep Neural Network Regularization for Feature Selection in Learning-to-Rank
by: Ashwini Rahangdale, et al.
Published: (2019-01-01)