LDAViewer: An Automatic Language-Agnostic System for Discovering State-of-the-Art Topics in Research Using Topic Modeling, Bidirectional Encoder Representations From Transformers, and Entity Linking
Advancements in knowledge are pivotal to academic progress, necessitating efficient methods for discovering the state-of-the-art in various fields. Existing approaches, however, are language-specific and lack automation, limiting their efficacy. This study aims to develop a language-agnostic softwar...
Main Authors: | Timothy Dillan, Dhomas Hatta Fudholi |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2023-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10147827/ |
Similar Items
-
Analysis of Health Research Topics in Indonesia Using the LDA (Latent Dirichlet Allocation) Topic Modeling Method
by: Yoga Sahria, et al.
Published: (2020-04-01) -
Survey of Automatic Labeling Methods for Topic Models
by: HE Dongbin, TAO Sha, ZHU Yanhong, REN Yanzhao, CHU Yunxia
Published: (2023-12-01) -
Estimation of Topic Similarity and Its Application to Measuring Stability of Topic Modeling
by: Sung-Chien Lin
Published: (2022-07-01) -
Topic Modeling for Support Ticket using Latent Dirichlet Allocation
by: Wiranto Wiranto, et al.
Published: (2022-12-01) -
Knowledge Graph Completion for the Chinese Text of Cultural Relics Based on Bidirectional Encoder Representations from Transformers with Entity-Type Information
by: Min Zhang, et al.
Published: (2020-10-01)