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

Sources of Law

Sources of Law

Laws form the essential rules set by governing authorities to shape and control societal behavior. In nursing, laws guide actions, safeguard patient rights, define nurses' scope of practice, and maintain professional standards. Understanding the legal framework governing nursing involves recognizing four primary sources of law: constitutional, statutory, administrative (regulatory), and common law.
Constitutional law is foundational, deriving from federal and state constitutions, and...

Genetic Lingo

Genetic Lingo

Legal Guidelines for Documentation

Legal Guidelines for Documentation

The legal guidelines for nursing documentation are essential for ensuring accurate, professional, and ethical recording of patient care. The guidelines are discussed here:

lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...

Force Classification

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

Cis-regulatory Sequences

Cis-regulatory Sequences

You might also read

Related Articles

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

Sort by

Same author

Emerging role and mechanism of HACE1 in the pathogenesis of neurodegenerative diseases: A promising target.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie·2024

Same author

Da-Chuan-Xiong Decoction Ameliorates Sodium Sensitivity and Plasma Norepinephrine via Attenuation of Brain Oxidative Stress in the DOCA-Salt Hypertensive Rats.

International journal of hypertension·2024

Same author

Degradation of triclosan by peroxydisulfate/peroxomonosulfate binary oxidants activation under thermal conditions: Efficiency and mechanism.

Journal of environmental management·2024

Same author

Tunable Adhesion for All-Dry Transfer of 2D Materials Enabled by the Freezing of Transfer Medium.

Advanced materials (Deerfield Beach, Fla.)·2024

Same author

Chromosome-level genome of spider Pardosa pseudoannulata and cuticle protein genes in environmental stresses.

Scientific data·2024

Same author

The efficacy and safety of fexuprazan in treating erosive esophagitis: a phase III, randomized, double-blind, multicenter study.

Journal of gastroenterology and hepatology·2024

Search research articles

Related Experiment Video

Updated: Jun 5, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

Generative named entity recognition framework for Chinese legal domain.

Xingliang Mao¹, Jie Jiang², Yongzhe Zeng³

¹School of Digital Media Engineering and Humanities, Hunan University of Technology and Business, Changsha, Hunan, China.

Peerj. Computer Science

|December 9, 2024

Summary

This study introduces a new framework for legal named entity recognition (NER) that improves accuracy in identifying complex legal entities. The approach enhances entity type prediction and boundary detection in legal texts.

Keywords:

Contrastive learning Legal text Multi-task training Named entity recognition

More Related Videos

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Published on: June 29, 2021

Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese

Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese

Published on: April 1, 2016

Related Experiment Videos

Last Updated: Jun 5, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Published on: June 29, 2021

Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese

Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese

Published on: April 1, 2016

Area of Science:

Natural Language Processing
Computational Linguistics
Legal Informatics

Background:

Named Entity Recognition (NER) is vital in NLP but challenging in the legal domain due to complex entity structures.
Current NER methods face difficulties in accurately defining boundaries and types of legal entities.
The legal domain necessitates specialized approaches for effective information extraction.

Purpose of the Study:

To develop a novel sequence-to-sequence framework for legal Named Entity Recognition (NER).
To enhance the accuracy of identifying complex legal entities and their types.
To overcome limitations of existing methods in legal text analysis.

Main Methods:

A novel sequence-to-sequence framework incorporating an entity-type-aware module.
Utilizing contrastive learning to improve entity type prediction.
Implementing a decoder with a copy mechanism for precise identification of legal entities.

Main Results:

The proposed framework significantly outperforms state-of-the-art methods on two legal datasets.
Achieved notable improvements in precision, recall, and F1 score for legal NER.
Demonstrated effective identification of complex legal entities without explicit tagging schemas.

Conclusions:

The developed framework offers a significant advancement in legal Named Entity Recognition.
The approach shows promise for future research in automated legal text analysis.
Effective entity recognition in legal texts is crucial for various legal applications.