Ȩ : »çÀÌÆ®¸Ê : ¹®ÀǸÞÀÏ : ÀüÀÚÀú³Î
      ¿¬±¸À±¸®À§¿øȸ ±ÔÁ¤
      ÆíÁýÀ§¿øȸ ±ÔÁ¤
      ³í¹®Åõ°í¾È³»/±ÔÁ¤
      ³í¹®ÀÛ¼º¾ç½Ä
      ³í¹®Åõ°í½Åû
      ³í¹®ÀÚ·á½Ç
      ÇÐȸÁö°ü·Ã FAQ
 
 
 
Ȩ > ÇÐȸÁö > ³í¹®ÀÚ·á½Ç
 
Á¦¸ñ Ãß»óÀû ÀÇ¹Ì Ç¥»óÀ» È°¿ëÇÑ »çÁø ÀÚ¸· ¿µÀÛ¹® Æò°¡
ÀúÀÚ ±èµ¿¼º
±Ç / È£ 24±Ç / 4È£
Ãâó 235-260
³í¹®°ÔÀçÀÏ 2016.12.31.
ÃÊ·Ï Kim, Dong-Sung. (2016). English Caption Writing Assessment Using Abstract Meaning Representation. The Linguistic Association of Korea Journal, 24(4), 235-260. Since story-telling has been used in evaluating the development of language skills, English language proficiency test such as TOEIC includes a caption writing test. This paper investigates how linguistically motivated features are used for automatically scoring a picture-description writing test. Specifically, we design to build scoring models with features under the principles of relevancy, appropriateness, and task-detailed description. For the experiment, we gather the caption writing corpus upon several images. We statistically compare different performances among 9 statistical assessment factors, revealing that Abstract Meaning Representation (AMR) produces the best results in predicting human raters scores. AMR shows the best performance in capturing the similar logico-semantic structure(s) among various sentential forms.
÷ºÎ
  12.±èµ¿¼º.pdf
  12.±èµ¿¼º.hwp
 
 
 
 °³ÀÎÁ¤º¸º¸È£Á¤Ã¥ : À̸ÞÀϹ«´Ü¼öÁý°ÅºÎ : »çÀÌÆ®¸Ê : À̸ÞÀϹ®ÀÇÇϱâ