Canonical Chinese Syntax Awareness Facilitated by an e–Learning Program

  • C. C. Lu
  • C. H. Lu
  • M. M. Lu
  • C. H. Hue
  • W. L. Hsu
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 109)


Being aware of syntactic structures is important in learning a new language. However, the syntactic concepts could be very elusive and vague and learning syntactic structures could be very dry and tedious.

In this study, based on our analysis of Chinese regulated verses in Tang Dynasty, we designed an e-learning program. The aim of the program is to make the language learners familiarized with the frequent grammatical structures of the language, alone with learning the vocabulary in the same semantic field.

The canonical Chinese syntactic patterns emerged from our corpus after delicate analysis, and a database which contained various word classes was created. Several steps were set up for helping the Chinese learners to make up one line in an antithetical couplet. First, the learner was asked to choose the topic, and then to choose the semantic frame and the canonical syntactic tablet. Second, one line in an antithetical couplet selected from our database was shown on the screen, and the learner’s task is to make up another line by choosing words from a fixed set offered by the program. Matched with the screening criteria coded in our program, several candidates were suggested for each slot of the couplet. The learner was asked to choose the words for his own preference and finished the poem. After that, the learner could start another new trial.

In order to examine the learning effect, a syntactic awareness test was constructed and given to the Chinese learners before and after the training session. In addition, some testing items were presented to the learners during the training session and their responses were recorded and graded, in order to assess their formative learning effect in using this e-learning program.


Chinese Learner Tang Dynasty Head Noun Semantic Field Syntactic Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Firth, J.R.: Papers in Linguistics,1934-1951. Oxford University Press, London (1957)Google Scholar
  2. 2.
    Goldberg, A.: Constructions: a construction grammar approach to argument structure. University of Chicago Press, Chicago (1995)Google Scholar
  3. 3.
    Hoey, M.: The hidden lexical clues of textual organization: a preliminary investigation into an unusual text from a corpus perspective. In: Burnard, L., McEnery, T. (eds.) Rethinking Language Pedagogy from a Corpus Perspective, pp. 31–42. Peter Lang, New York (2000)Google Scholar
  4. 4.
    Hoey, M.: Lexical priming: A new theory of words and language. Routledge, London (2005)Google Scholar
  5. 5.
    Hunston, S., Francis, G.: Pattern Grammar: a corpus-driven approach tothe lexical grammar of English. John Benjamins, Amsterdam (2000)Google Scholar
  6. 6.
    Li, P., Farkas, I., MacWhinney, B.: Early lexical development in a self-organizing neural network. Neural Networks 17, 1345–1362 (2004)CrossRefGoogle Scholar
  7. 7.
    Ravelli, L.J.: Signaling the organization of written texts: Hyper-Themes in management and history. In: Ravelli, L., Ellis, R. (eds.) Analyzing Academic Writing: Contextualized Frameworks, Continuum, London, pp. 105–130 (2004)Google Scholar
  8. 8.
    Sinclair, J.M.: Corpus, Concordance, Collocation. Oxford University Press, Oxford (1991)Google Scholar
  9. 9.
    Stubbs, M.: Words and Phrases: Corpus Studies of Lexical Semantics. Blackwell, Oxford (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • C. C. Lu
    • 1
  • C. H. Lu
    • 2
  • M. M. Lu
    • 3
  • C. H. Hue
    • 4
  • W. L. Hsu
    • 2
  1. 1.National Hsinchu Univ of Education
  2. 2.Academia Sinica
  3. 3.Natl Univ of Tainan
  4. 4.Natl Taiwan Univ

Personalised recommendations