Complexity: Activity 3
Syntactic complexity – Verb arguments
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As pointed out in the Overview of Complexity, there are many possible measures of the complexity of spoken learner language. One such measure is a count of the mean number of verb arguments used for each verb in a speech sample.
A verb argument is “an element of clause structure – either a subject, direct or indirect object, an adverbial or a prepositional complement” (Bygate, 1999, pp.196-197). The mean number of verb arguments is calculated by dividing the total number of verb arguments by the total number of finite verbs. A high mean number of verb arguments will indicate more complex structures. The following table shows that the sentence length expands and the sentence structure becomes more complex as the same verb ‘있다 (to exist)’ is used with more arguments.
Verb arguments Examples from Jigsaw task 0 19 S: 아니요, 없어요. 1 3 S: 어, 하얀색이에요?
27 S: 그리고 나무 있어요?2 18 A: 아 아 집 앞에 나무 있어요? 3 9 S: 다섯. 제 그림도 집 앞으로 창문 다섯 개 있어요.
Look at the transcripts of the Jigsaw and Comparison tasks. Consider the 23 turns in line 45 though 67 in the Jigsaw task and all the turns in the Comparison task. Count the number of finite verbs in both tasks. Then, count the number of verb arguments per verb in both tasks. False starts and repetitions are not counted. You can refer to examples of Korean verb arguments in the table above.
Fill in the table below.
Verb arguments Jigsaw Comparison Sophia Anna B Sophia Anna B 0 1 2 3 4 Total number of verb arguments Total number of finite verbs The mean number of verb arguments
Looking at the table that you have just filled out, what kinds of patterns do you find in counting the mean number of verb arguments in each task? Which task seems to elicit more complex sentence structures based on this analysis?
Please type your answers to the questions in the box below.
When you have finished typing your answer, click to compare your response with the Learner Language staff response.
The number of verb arguments counted in both tasks is as follows.
| Verb arguments | Jigsaw | Comparison | ||
|---|---|---|---|---|
| Sophia | Anna B | Sophia | Anna B | |
| 0 | 5 | 1 | 3 | 0 |
| 1 | 5 | 1 | 0 | 3 |
| 2 | 6 | 4 | 2 | 3 |
| 3 | 1 | 0 | 4 | 4 |
| 4 | 0 | 0 | 1 | 0 |
| Total number of verb arguments | 20 | 9 | 20 | 21 |
| Total number of finite verbs | 17 | 6 | 10 | 10 |
| The mean number of verb arguments | 1.17 | 1.5 | 2 | 2.1 |
In the Jigsaw task, Sophia and Anna B both use two-argument verbs most frequently; they do not use four-argument verbs. The two learners produce more various and complex sentences in the Comparison task than in the Jigsaw task. But they do this in different ways. In the Comparison task, Anna B produces more complex sentences by using three-argument verbs, unused in the Jigsaw task. Sophia produces more sentences with zero- and three-argument, while reducing the number of one- and two-argument verbs. In addition, she produces one four-argument verb – the only one produced by either learner on either task. For both learners, there is a higher mean number of verb arguments in the Comparison task than in the Jigsaw task (For Sophia it is 2 compared to 1.17, for Anna B, 2.1 compared to 1.5).
