Enhanced Web document retrieval using automatic query expansion

2003 ◽  
Vol 55 (1) ◽  
pp. 29-40 ◽  
Author(s):  
M. Shamim Khan ◽  
Sebastian Khor
Keyword(s):  
Query Expansion ◽  
Document Retrieval ◽  
Web Document

A personalized query expansion approach for engineering document retrieval

2014 ◽  
Vol 28 (4) ◽  
pp. 344-359 ◽  
Author(s):  
Gyeong June Hahm ◽  
Mun Yong Yi ◽  
Jae Hyun Lee ◽  
Hyo Won Suh
Keyword(s):  
Query Expansion ◽  
Document Retrieval

Open-vocabulary spoken-document retrieval based on query expansion using related web documents

2008 ◽  
Author(s):  
Makoto Terao ◽  
Takafumi Koshinaka ◽  
Shinichi Ando ◽  
Ryosuke Isotani ◽  
Akitoshi Okumura
Keyword(s):  
Query Expansion ◽  
Document Retrieval ◽  
Spoken Document Retrieval ◽  
Web Documents

scholarly journals Optimasi Pembobotan pada Query Expansion dengan Term Relatedness to Query-Entropy based (TRQE)

2015 ◽  
Vol 6 (3) ◽  
Author(s):  
Resti Ludviani ◽  
Khadijah F. Hayati ◽  
Agus Zainal Arifin ◽  
Diana Purwitasari
Keyword(s):  
Query Expansion ◽  
Retrieval System ◽  
Document Retrieval ◽  
Retrieval Performance ◽  
Term Weighting ◽  
New Approach ◽  
Term Selection ◽  
Relevance Evaluation ◽  
Feedback Module ◽  
Pseudo Feedback

Abstract. An appropriate selection term for expanding a query is very important in query expansion. Therefore, term selection optimization is added to improve query expansion performance on document retrieval system. This study proposes a new approach named Term Relatedness to Query-Entropy based (TRQE) to optimize weight in query expansion by considering semantic and statistic aspects from relevance evaluation of pseudo feedback to improve document retrieval performance. The proposed method has 3 main modules, they are relevace feedback, pseudo feedback, and document retrieval. TRQE is implemented in pseudo feedback module to optimize weighting term in query expansion. The evaluation result shows that TRQE can retrieve document with the highest result at precission of 100% and recall of 22,22%. TRQE for weighting optimization of query expansion is proven to improve retrieval document.     Keywords: TRQE, query expansion, term weighting, term relatedness to query, relevance feedback Abstrak..Pemilihan term yang tepat untuk memperluas queri merupakan hal yang penting pada query expansion. Oleh karena itu, perlu dilakukan optimasi penentuan term yang sesuai sehingga mampu meningkatkan performa query expansion pada system temu kembali dokumen. Penelitian ini mengajukan metode Term Relatedness to Query-Entropy based (TRQE), sebuah metode untuk mengoptimasi pembobotan pada query expansion dengan memperhatikan aspek semantic dan statistic dari penilaian relevansi suatu pseudo feedback sehingga mampu meningkatkan performa temukembali dokumen. Metode yang diusulkan memiliki 3 modul utama yaitu relevan feedback, pseudo feedback, dan document retrieval. TRQE diimplementasikan pada modul pseudo feedback untuk optimasi pembobotan term pada ekspansi query. Evaluasi hasil uji coba menunjukkan bahwa metode TRQE dapat melakukan temukembali dokumen dengan hasil terbaik pada precision  100% dan recall sebesar 22,22%.Metode TRQE untuk optimasi pembobotan pada query expansion terbukti memberikan pengaruh untuk meningkatkan relevansi pencarian dokumen.Kata Kunci: TRQE, ekspansi query, pembobotan term, term relatedness to query, relevance feedback

scholarly journals Extraction of Effective Textual and Semantic Features in Learning to Rank for Web Document Retrieval

2021 ◽  
Vol 36 (4) ◽  
pp. 1081-1112
Author(s):  
Mohaddeseh Mahjoob ◽  
Faezeh Ensan ◽  
Sanaz Keshvari ◽  
Parastoo Jafarzadeh ◽  
Mohammadamin keyvanzad ◽  
...  
Keyword(s):  
Learning To Rank ◽  
Document Retrieval ◽  
Semantic Features ◽  
Web Document
Sign in / Sign up

Export Citation Format

Share Document