Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Extraction: Advanced Methods00:56

Extraction: Advanced Methods

564
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
564
Cluster Sampling Method01:20

Cluster Sampling Method

13.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.0K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

3.5K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
3.5K
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

15.1K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
15.1K
Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

16.8K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
16.8K
Tip-of-the-Tongue Phenomenon01:10

Tip-of-the-Tongue Phenomenon

264
The tip-of-the-tongue (TOT) phenomenon is a cognitive experience characterized by a temporary inability to retrieve specific information from memory despite having a strong feeling of knowing the information. Although individuals cannot access the target word or detail, they frequently recall related elements, such as its initial letter, syllable count, or context. This partial retrieval often causes frustration, as one might recognize a familiar face or know that a name starts with a specific...
264

You might also read

Related Articles

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

Sort by
Same author

Psychological correlates of problematic short video use risk profiles: A latent profile and network analysis.

BMC psychology·2026
Same author

Sex-specific temporal symptom networks in gaming disorder - depression comorbidity: longitudinal validation of the I-PACE model.

Addictive behaviors·2026
Same author

Digital Phenotyping of High-Risk Gaming Behavior Using Wearable Devices.

Alpha psychiatry·2026
Same author

Association between multiple infection patterns of HPV33 and the risk of cervical carcinogenesis.

Frontiers in microbiology·2026
Same author

Predictive Value of Incorporating Principal Diagnosis into CGA for Short-Term Functional Recovery in an ACE Unit: A Retrospective Study.

Clinical interventions in aging·2026
Same author

Disrupted brain functional networks in adolescents and young adults with gaming disorder during social interaction: An fNIRS study.

Psychological medicine·2026

Related Experiment Video

Updated: Oct 4, 2025

Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition
12:49

Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition

Published on: July 13, 2019

17.3K

TextRank Keyword Extraction Algorithm Using Word Vector Clustering Based on Rough Data-Deduction.

Ning Zhou1, Wenqian Shi1, Renyu Liang1

  • 1School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.

Computational Intelligence and Neuroscience
|February 7, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces RDD-WRank, an improved TextRank algorithm for keyword extraction. It enhances keyword extraction accuracy by incorporating rough data reasoning and word vector clustering.

More Related Videos

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

706
A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
10:23

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

Published on: July 11, 2025

271

Related Experiment Videos

Last Updated: Oct 4, 2025

Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition
12:49

Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition

Published on: July 13, 2019

17.3K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

706
A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
10:23

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

Published on: July 11, 2025

271

Area of Science:

  • Natural Language Processing
  • Information Retrieval
  • Artificial Intelligence

Background:

  • Traditional TextRank keyword extraction relies on local term co-occurrence, limiting its scope.
  • Existing methods struggle to capture broader semantic relationships within documents.

Purpose of the Study:

  • To propose an improved TextRank algorithm, RDD-WRank, for more comprehensive keyword extraction.
  • To enhance keyword extraction accuracy by integrating rough data reasoning and word vector clustering.

Main Methods:

  • Developed RDD-WRank by combining rough data reasoning for broader association mining.
  • Integrated Word2Vec word embeddings and clustering to adjust node importance in the TextRank graph.
  • Utilized Wikipedia as an external knowledge base for word vector integration.

Main Results:

  • RDD-WRank demonstrated significantly improved keyword extraction accuracy compared to traditional TextRank and Word2Vec-enhanced TextRank.
  • Rough data reasoning effectively expanded the scope of association mining for candidate keywords.
  • Word vector clustering refined the voting importance of nodes, enhancing precision.

Conclusions:

  • The proposed RDD-WRank algorithm offers a more effective approach to keyword extraction.
  • Integrating rough data reasoning and word vector clustering is a promising strategy for improving NLP tasks.
  • The RDD-WRank method provides a more comprehensive and accurate keyword extraction solution.