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Workshop on Chinese Lexical Semantics

CLSW 2019: Chinese Lexical Semantics pp 381–392Cite as

Sentence-Level Readability Assessment for L2 Chinese Learning

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11831))

Abstract

Automatic assessment of sentence readability level can support educators in selecting sentence examples suitable for different learning levels to complement teaching materials. Although there exists extensive research on document-level and passage-level Chinese readability assessment, the sentence-level evaluation remains little explored. We bridge the gap by providing a research framework and a large corpus of nearly 40,000 sentences with ten-level readability annotation. We design experiments to analyze the influence of 88 linguistic features on sentence complexity and results suggest that the linguistic features can significantly improve the predictive performance with the highest of 70.78% distance-1 adjacent accuracy. Model comparison also confirms that our proposed set of features can reduce the bias in prediction without adding variances. We hope that our corpus, feature sets, and experimental validation can provide educators and linguists with more language resources, enlightenment, and automatic tools for future related research.

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Notes

  1. 1.

    http://www.aihanyu.org/.

  2. 2.

    The seven universities participating in text-books survey are Renmin University of China, Beijing Language and Culture University, Sun Yat-sen University, Jinan University, South China Normal University, Huaqiao University, and Fujian Normal University.

  3. 3.

    http://www.ltp-cloud.com/.

  4. 4.

    http://www.niuparser.com/.

References

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Acknowledgements

This work was supported by National Social Science Fund (Grant No. 17BGL068). We thank undergraduate students Zhiwei Wu, Yuansheng Wang, Xu Zhang, Yuan Chen, Hanwu Chen, Licong Tan, and Hao Zhang for their helpful assistance and support.

Author information

Authors and Affiliations

  1. School of Liberal Arts, Renmin University of China, Beijing, China

    Dawei Lu

  2. School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China

    Xinying Qiu

  3. School of Software Engineering, South China University of Technology, Guangzhou, China

    Yi Cai

Authors
  1. Dawei Lu
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  2. Xinying Qiu
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  3. Yi Cai
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Corresponding author

Correspondence to Xinying Qiu .

Editor information

Editors and Affiliations

  1. National Taiwan Normal University, Taipei, Taiwan

    Jia-Fei Hong

  2. Beijing Information Science and Technology University, Beijing, China

    Yangsen Zhang

  3. Beijing Language and Culture University, Beijing, China

    Pengyuan Liu

Appendix

Appendix

Linguistic Features

Category

Sub-category

Feature definition

Shallow features

Character features

1. Percentage of most-common characters per sentence

2. Percentage of second-most-common characters per sentence

3. Percentage of all common-characters per sentence

4. Percentage of low-stroke-count characters per sentence

5. Percentage of medium-stroke-count characters per sentence

6. Percentage of high-stroke-count characters per sentence

7. Average number of strokes per word per sentence

8. Percentage of HSK1 to HSK3-characters per sentence

9. Percentage of HSK4 to HSK5-characters per sentence

10. Percentage of HSK6-characters per sentence

11. Percentage of not-HSK-characters per sentence

Word features

12. Average number of characters per word per sentence

13. Average number of characters per unique word per sentence

14. Number of two-character words per sentence

15. Percentage of two-character words per sentence

16. Number of three-character words per sentence

17. Percentage of three-character words per sentence

18. Number of four-character words per sentence

19. Percentage of four-character words per sentence

20. Number of five-up-character words per sentence

21. Percentage of five-up-character words per sentence

22. Percentage of HSK1 to HSK3-words per sentence

23. Percentage of HSK4 to HSK5-words per sentence

24. Percentage of HSK6-words per sentence

25. Percentage of Not-HSK-words per sentence

Sentence features

26. Number of multi-character words per sentence

27. Number of words per sentence

28. Number of characters per sentence

29. Number of characters (including punctuations, numerical, and symbols) per sentence

POS Features

Adjectives

30. Percentage of adjectives per sentence

31. Percentage of unique adjectives per sentence

32. Number of unique adjectives per sentence

33. Number of adjectives per sentence

Functional words

34. Percentage of functional words per sentence

35. Percentage of unique functional words per sentence

36. Number of unique functional words per sentence

37. Number of functional words per sentence

Verbs

38. Percentage of verbs per sentence

39. Number of unique verbs per sentence

40. Percentage of unique verbs per sentence

41. Number of verbs per sentence

Nouns

42. Percentage of nouns per sentence

43. Number of unique nouns per sentence

44. Percentage of unique nouns per sentence

45. Number of nouns per sentence

46. Percentage of All-Nouns per sentence

47. Number of unique All-Nouns per sentence

48. Percentage of unique All-Nouns per sentence

49. Number of All-Nouns per sentence

Content words

50. Percentage of content words per sentence

51. Number of unique content words per sentence

52. Percentage of unique content words per sentence

53. Number of content words per sentence

Idioms

54. Percentage of idioms per sentence

55. Number of unique idioms per sentence

56. Percentage of unique idioms per sentence

57. Number of idioms per sentence

Adverbs

58. Percentage of adverbs per sentence

59. Percentage of unique adverbs per sentence

60. Number of unique adverbs per sentence

61. Number of adverbs per sentence

Syntactic features

Phrases

62. Total number of noun phrases per sentence

63. Total number of verbal phrases per sentence

64. Total number of prepositional phrases per sentence

65. Average length of noun phrases per sentence

66. Average length of verbal phrases per sentence

67. Average length of prepositional phrases per sentence

Clauses

68. Number of punctuation-clauses per sentence

69. Average dependency distance per sentence

70. Maximum dependency distance per sentence

Sentences

71. Height of parse tree per sentence

72. Total number of dependency distances per sentence

73. Average number of dependency distances per sentence

Discourse features

Entity density

74. Total number of entities per sentence

75. Total number of unique entities per sentence

76. Percentage of entities per sentence

77. Percentage of unique entities per sentence

78. Percentage of named entities per sentence

79. Percentage of named entities against total number of entities per sentence

80. Percentage of Not-NE nouns per sentence

81. Number of Not-NE nouns per sentence

82. Number of Not-Entity nouns per sentence

Cohesion

83. Percentage of conjunctions per sentence

84. Number of unique conjunctions per sentence

85. Percentage of unique conjunctions per sentence

86. Percentage of pronouns per sentence

87. Number of unique pronouns per sentence

88. Percentage of unique pronouns per sentence

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Lu, D., Qiu, X., Cai, Y. (2020). Sentence-Level Readability Assessment for L2 Chinese Learning. In: Hong, JF., Zhang, Y., Liu, P. (eds) Chinese Lexical Semantics. CLSW 2019. Lecture Notes in Computer Science(), vol 11831. Springer, Cham. https://doi.org/10.1007/978-3-030-38189-9_40

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  • DOI: https://doi.org/10.1007/978-3-030-38189-9_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-38188-2

  • Online ISBN: 978-3-030-38189-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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