Word frequency and text complexity: an eye-tracking study of young Russian readers

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Abstract

Although word frequency is often associated with the cognitive load on the reader and is widely used for automated text complexity assessment, to date, no eye-tracking data have been obtained on the effectiveness of this parameter for text complexity prediction for the Russian primary school readers. Besides, the optimal ways for taking into account the frequency of individual words to assess an entire text complexity have not yet been precisely determined. This article aims to fill these gaps. The study was conducted on a sample of 53 children of primary school age. As a stimulus material, we used 6 texts that differ in the classical Flesch readability formula and data on the frequency of words in texts. As sources of the frequency data, we used the common frequency dictionary based on the material of the Russian National Corpus and DetCorpus - the corpus of literature addressed to children. The speed of reading the text aloud in words per minute averaged over the grades was employed as a measure of the text complexity. The best predictive results of the relative reading time were obtained using the lemma frequency data from the DetCorpus. At the text level, the highest correlation with the reading speed was shown by the text coverage with a list of 5,000 most frequent words, while both sources of the lists - Russian National Corpus and DetCorpus - showed almost the same correlation values. For a more detailed analysis, we also calculated the correlation of the frequency parameters of specific word forms and lemmas with three parameters of oculomotor activity: the dwell time, fixations count, and the average duration of fixations. At the word-by-word level, the lemma frequency by DetCorpus demonstrated the highest correlation with the relative reading time. The results we obtained confirm the feasibility of using frequency data in the text complexity assessment task for primary school children and demonstrate the optimal ways to calculate frequency data.

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Table 1. Main linguistic parameters of the texts used in the experiment (FD RNC is a frequency dictionary based on Russian National Corpus,  DetCorpus is a corpus of literature addressed to children)

Параметр текста

Text 1. Tractor

Text 2. Umka

Text 3. In the grass

Text 4. Mouse

Text 5. Flowers

Text 6. Dog

FRE (Oborneva)

49

78

75

66

15

80

Text coverage by the list 5000 (FD RNC)

84%

85%

35%

89%

62%

92%

Average word length

6.4

4.8

5.6

5.6

6.8

4.1

Text coverage by the list 5000 (DetCorpus)

81%

83%

61%

93%

69%

96%

Percent of words with
 ipm < 5 (FD RNC)

8%

14%

43%

4%

21%

2%

Percent of words with
ipm < 5 (DetCorpus)

11%

12%

22%

4%

24%

0%

Average log word frequency
(FD RNC)

4.5

4.2

3.6

4.7

3.8

4.9

Average log word frequency
(DetCorpus)

4.2

4.6

3.8

4.8

4

4.9

 

Table 2. Word-by-word values of word length, frequency and eye movement parameters (FD RNC is a frequency dictionary based on Russian National Corpus,  DetCorpus is a corpus of literature addressed to children)

Word form

мальчики

гладиолусов

себе

современный

Lemma

мальчик

гладиолус

себя

современный

Length of word form in characters

8

11

4

11

Length of word form in syllables

2

4

2

4

Lemma frequency by FD RNC, ipm

188

0

2272

236

Lemma frequency by DetCorpus, ipm

597

1.1

2243

14

Word form frequency by FD RNC, ipm

19

0

90

33

Word form frequency by DetCorpus, ipm

91

0.4

86

4

Dwell time, %

0.026

0.089

0.019

0.032

Fixation duration, ms

257

288

255

250

Fixation count

3.22

9.15

2.46

4.43

 

Pic. 1. An example of the analyzed data of oculomotor activity

 

Fig. 2. Average reading speed of the texts by students of grades 1–3

 Table 3. Correlation analysis of oculomotor activity parameters with word frequency parameters (Spearman correlation, bold values have p-value <0.05)

Parameter

Average reading speed

Average word length

-0.83

FRE(Oborneva)

0.66

Text coverage by the list 5000 (FD RNC)

0.89

Text coverage by the list 5000 (DetCorpus)

0.89

Percent of words with ipm < 5 (FD RNC)

-0.77

Percent of words with ipm < 5 (DetCorpus)

-0.83

Average log word frequency (FD RNC)

0.78

Average log word frequency (DetCorpus)

0.85

 

 Table 4. Correlation analysis of oculomotor activity parameters and linguistic parameters of word forms (Spearman correlation, bold values have a p-value <0.05)

Parametr

Dwell time

Fixation duration

Fixation count

Length of word form in characters

0.53

-0.02

0.73

Length of word form in syllables

0.36

-0.09

0.55

Lemma frequency by FD RNC, ipm

0.55

0.49

0.46

Lemma frequency by DetCorpus, ipm

0.59

0.42

0.54

Word form frequency by FD RNC, ipm

0.58

0.47

0.53

Word form frequency by DetCorpus, ipm

0.58

0.42

0.53

 

Table 5. An example of the output of the Textometr tool (Russian as a native language section) for the texts from the experiment

Text

Structural complexity

Lexical complexity

Estimated age

Text 1. Tractor

4

3

9–10 years

Text 2. Umka

3

3

9–10 years

Text 3. In the grass

2

7

9–10 years

Text 4. Mouse

3

1

7–8 years

Text 5. Flowers

9

6

13–15 years

Text 6. Dog

2

1

7–8 years

 

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About the authors

Antonina N. Laposhina

Pushkin State Russian Language Institute

Email: ANLaposhina@pushkin.institute
ORCID iD: 0000-0003-0693-7657

leading expert of the Laboratory of Cognitive and Linguistic Studies

6 Akademika Volgina street, Moscow, 117485, Russia

Maria Yu. Lebedeva

Pushkin State Russian Language Institute

Email: MULebedeva@pushkin.institute
ORCID iD: 0000-0002-9893-9846

holds a PhD in Philology and is a leading researcher of the Laboratory of Cognitive and Linguistic Studies, Associate Professor of the Department of Methods of Teaching Russian as a Foreign Language

6 Akademika Volgina street, Moscow, 117485, Russia

Alexandra A. Berlin Khenis

Pushkin State Russian Language Institute

Author for correspondence.
Email: alexa.munxen@gmail.com
ORCID iD: 0000-0003-2034-1526

specialist of the Laboratory of Cognitive and Linguistic Studies

6 Akademika Volgina street, Moscow, 117485, Russia

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