Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Cross-Domain Feature Enhancement-Based Password Guessing Method for Small Samples.

Entropy (Basel, Switzerland)·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 14, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

A Hierarchical Multi-Task Learning Framework for Semantic Annotation in Tabular Data.

Jie Wu¹, Mengshu Hou¹

¹School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Entropy (Basel, Switzerland)

|August 29, 2024

Summary

This study introduces a unified multi-task learning framework for understanding table semantics. It improves column type identification and relationship detection by jointly learning these tasks, enhancing data analysis.

Keywords:

multi-task learning natural language processing table interpretation table semantic annotation tabular data

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

Related Experiment Videos

Last Updated: Jun 14, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

Area of Science:

Data Science
Artificial Intelligence
Machine Learning

Background:

Understanding table semantics is crucial for data utilization and analysis.
Many tables lack annotations, requiring identification of column types and relationships.
Existing models often address subtasks independently, leading to errors and missed constraints.

Purpose of the Study:

To develop a unified multi-task learning framework for comprehensive table semantic understanding.
To improve data quality, integration, and analysis by accurately identifying table components and their relationships.
To overcome limitations of independent subtask models by leveraging inter-task dependencies.

Main Methods:

Proposed a unified multi-task learning framework.
Integrated column named entity recognition, column type identification, and inter-column relationship detection.
The model utilizes only internal tabular data information, avoiding external knowledge graphs.

Main Results:

The unified framework demonstrated superior performance across various tasks.
Joint learning of related tasks improved individual subtask performance.
The model achieved robust performance even with limited input information.

Conclusions:

Unified multi-task learning is effective for table semantic recognition and comprehension.
Integrating related tasks enhances model generalization and accuracy.
The proposed framework offers a robust solution for analyzing unannotated tabular data.