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Related Concept Videos

What is a Frequency Distribution00:51

What is a Frequency Distribution

A frequency is the number of times a value of the data occurs. The sum of all the frequency values represents the total number of students included in the sample. It is commonly used to group data of quantitative types. Frequency distributions can be displayed in a table, histogram, line graph, dot plot, or pie chart, just to name a few. A histogram is a graphical representation of tabulated frequencies, shown as adjacent rectangles, erected over discrete intervals (bins), with an area equal to...
Construction of Frequency Distribution01:15

Construction of Frequency Distribution

A frequency distribution table can be constructed using the steps given below.
First, make a table with two columns—one with the title of the data that needs to be organized, and the other column for frequency. [Draw a third column for tally marks if needed]. Then, take a look at the items given in the data set and decide if an ungrouped frequency distribution table or a grouped frequency distribution table would be more suitable. If there are large sets of different values, then it is best to...
Relative Frequency Distribution00:55

Relative Frequency Distribution

A relative frequency distribution is the proportion or fraction of times a value occurs in a data set. To find the relative frequencies, one can divide each frequency by the total number of data points in the sample. It is very similar to a regular frequency distribution, except that instead of reporting how many data values fall in a class, a relative frequency distribution reports the fraction of data values that fall in a class. These fractions or proportions are called relative frequencies...
Relative Frequency Histogram01:14

Relative Frequency Histogram

The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
Determination of Expected Frequency01:08

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
Relation of DFT to z-Transform01:20

Relation of DFT to z-Transform

The Discrete Fourier Transform (DFT) is a crucial tool for analyzing the frequency content of discrete-time signals. It converts a sequence of N samples from the time domain into its corresponding sequence in the frequency domain, where each sample represents a specific frequency component.
To understand how the DFT works, it's helpful to consider the z-transform, which is a method for representing discrete sequences in the complex frequency domain. The z-transform involves summing the terms of...

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Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese
08:08

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

Published on: April 1, 2016

Zipfian frequency distributions facilitate word segmentation in context.

Chigusa Kurumada1, Stephan C Meylan, Michael C Frank

  • 1Department of Linguistics, Stanford University, United States. kurumada@stanford.edu

Cognition
|April 6, 2013
PubMed
Summary

Learning artificial languages with skewed word frequencies (Zipfian distribution) improves word segmentation, unlike uniform distributions. High-frequency words aid processing new sentences, benefiting language acquisition models.

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Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Psycholinguistics

Background:

  • Natural language word frequencies follow a skewed Zipfian distribution.
  • Previous language acquisition simulations often used uniform word frequencies, potentially misrepresenting real-world learning conditions.

Purpose of the Study:

  • To investigate the impact of Zipfian word frequency distributions on artificial language acquisition.
  • To compare learning outcomes between uniform and Zipfian frequency distributions in artificial language tasks.

Main Methods:

  • Utilized two artificial language learning paradigms: a forced-choice task and an orthographic segmentation task.
  • Analyzed participant performance across varying word frequency distributions, including Zipfian and uniform.

Main Results:

  • Learners demonstrated robust identification of word forms irrespective of frequency distribution.
  • Zipfian distributions, characterized by high-frequency words, significantly enhanced word segmentation in context.
  • Computational models using 'chunking' better replicated observed learning patterns than 'transition finding' models.

Conclusions:

  • Skewed word frequencies (Zipfian distribution) are beneficial for word segmentation during language acquisition.
  • High-frequency words play a crucial role in facilitating sentence processing and learning.
  • Chunking mechanisms are more effective than transition-based approaches for modeling frequency-dependent learning in artificial languages.