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Related Concept Videos

Correlation01:09

Correlation

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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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Correlation and Regression00:53

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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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Correlations02:20

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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Cause and Effect01:53

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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Theory of Attribution I: Correspondent Inference Theory01:15

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Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...
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Correlation and Causation01:27

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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Self-selective attention using correlation between instances for distant supervision relation extraction.

Yanru Zhou1, Limin Pan1, Chongyou Bai1

  • 1Information System and Security & Countermeasures Experimental Center, Beijing Institute of Technology, Beijing 100081, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 24, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel self-selective attention method for distant supervision relation extraction. It improves performance by better utilizing correlations between instances and all correctly labeled data within a bag.

Keywords:
Convolution neural networkDistant supervision relation extractionSelf-attention mechanism

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Area of Science:

  • Natural Language Processing
  • Information Extraction
  • Machine Learning

Background:

  • Distant supervision is common for extracting relational facts from text.
  • Traditional methods overlook instance correlations within bags, limiting performance.

Purpose of the Study:

  • To propose a self-selective attention method for distant supervision relation extraction.
  • To enhance the utilization of instance correlations and supervision information.

Main Methods:

  • Employs convolution and self-attention for instance encoding and semantic vector representation.
  • Weights correctly labeled instances based on inter-instance correlations.
  • Calculates a bag vector representation through weighted summation.

Main Results:

  • Effectively leverages information from all correctly labeled instances in a bag.
  • Demonstrates improved performance compared to baseline methods on the NYT dataset.

Conclusions:

  • The proposed self-selective attention method enhances distant supervision relation extraction.
  • Better utilization of instance correlations leads to superior results.