Results 31 to 40 of about 1,872,988 (376)

A query expansion method based on topic modeling and DBpedia features

International Journal of Information Management Data Insights, 2021
Query Expansion (QE) is a method used for improving Information Retrieval (IR) by adding the terms that are almost selected from feedback documents, and similar to the user query terms.
Sarah Dahir, Abderrahim El Qadi
doaj   +1 more source

Perbandingan Algoritma Selection Feature Query Expansion Ranking dan Symmetrical Uncertainty Pada Instagram Terkait Sengketa ZEE di Perairan Natuna [PDF]

, 2021
Algoritma Naïve Bayes adalah untuk menggabungkan probabilitas kata-kata dan kategori untuk memperkirakan probabilitas kategorikan dokumen. Naïve Bayes ini merupakan algoritma klasifikasi yang sering digunakan dalam sebuah penelitian salah satunya ...
Aditya, Christian Sri Kusuma, Aziz, Dzurrifqi, Hayatin, Nur   +2 more
core   +1 more source

HASHTAG AND HIGHEST SCORED TERMS FOR EXPANDING QUERY

Journal of ICT, 2017
Communicating in short messages, such as using microblogs, was becoming more popular currently. Twitter https://twitter.com supports microblogs and retrieval of the blogs by users.
Wahyu Wibowo, Widodo Widodo
doaj   +3 more sources

Word Embeddings-Based Pseudo Relevance Feedback Using Deep Averaging Networks for Arabic Document Retrieval

Journal of Information Science Theory and Practice, 2021
Pseudo relevance feedback (PRF) is a powerful query expansion (QE) technique that prepares queries using the top k pseudo-relevant documents and choosing expansion elements.
Farhan Yasir Hadi, Noah Shahrul Azman Mohd, Mohd Masnizah, Atwan Jaffar   +3 more
doaj   +1 more source

An Algorithm of Query Expansion for Chinese EMR Retrieval by Improving Expansion Term Weights and Retrieval Scores

IEEE Access, 2020
Query expansion (QE) has been widely used in electronic medical record (EMR) retrieval for assisted diagnosis and clinical research. However, existing QE algorithms haven't achieved satisfactory performance in Chinese EMR retrieval, and one noticeable ...
Songchun Yang, Xiangwen Zheng, Xiangfei Yin, Huajian Mao, Dongsheng Zhao   +4 more
doaj   +1 more source

A Query Expansion Benchmark on Social Media Information Retrieval: Which Methodology Performs Best and Aligns with Semantics?

Computers, 2023
This paper presents a benchmarking survey on query expansion techniques for social media information retrieval, with a focus on comparing the performance of methods using semantic web technologies.
Evangelos A. Stathopoulos, Anastasios I. Karageorgiadis, Alexandros Kokkalas, Sotiris Diplaris, Stefanos Vrochidis, Ioannis Kompatsiaris   +5 more
doaj   +1 more source

Using relevance feedback in expert search [PDF]

, 2007
In Enterprise settings, expert search is considered an important task. In this search task, the user has a need for expertise - for instance, they require assistance from someone about a topic of interest. An expert search system assists users with their
Macdonald, C., Ounis, I.
core   +2 more sources

CEQE: Contextualized Embeddings for Query Expansion [PDF]

, 2021
In this work we leverage recent advances in context-sensitive language models to improve the task of query expansion. Contextualized word representation models, such as ELMo and BERT, are rapidly replacing static embedding models. We propose a new model, Contextualized Embeddings for Query Expansion (CEQE), that utilizes query-focused contextualized ...
Naseri, Shahrzad, Dalton, Jeffrey, Yates, Andrew, Allan, James   +3 more
openaire   +4 more sources

Re-ranking vehicle re-identification with orientation-guide query expansion

International Journal of Distributed Sensor Networks, 2022
Vehicle re-identification, which aims to retrieve information regarding a vehicle from different cameras with non-overlapping views, has recently attracted extensive attention in the field of computer vision owing to the development of smart cities. This
Xue Zhang, Xiushan Nie, Ziruo Sun, Xiaofeng Li, Chuntao Wang, Peng Tao, Sumaira Hussain   +6 more
doaj   +1 more source

QA4PRF: A Question Answering Based Framework for Pseudo Relevance Feedback

IEEE Access, 2021
Pseudo relevance feedback (PRF) automatically performs query expansion based on top-retrieved documents to better represent the user’s information need so as to improve the search results.
Handong Ma, Jiawei Hou, Chenxu Zhu, Weinan Zhang, Ruiming Tang, Jincai Lai, Jieming Zhu, Xiuqiang He, Yong Yu   +8 more
doaj   +1 more source