Vocabulary and neural networks in the computational assessment of texts written by second-language learners  

Oleh:

Meara, Paul ; Rodgers, Catherine ; Jacobs, Gabriel

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: SYSTEM: An International Journal of Educational Technology and Applied Linguistics (Full Text) vol. 28 no. 3 (Sep. 2000), page 345-354.
Topik: Neural network; Computational assessment; Vocabulary; French
Fulltext: Meara_Paul.pdf (835.39KB)

Isi artikel

This paper explores the potential of a neural network in language assessment. Many examination systems rely on subjective judgments made by examiners as a way of grading the writing of non-native speakers. Some research (e.g. Engber, 1995. The relationship of lexical pro®ciency to the quality of ESL compositions. Journal of Second Language Writing 4(2), 139±155) has shown that these subjective judgements are in¯uenced to a very large extent by the lexical choices made by candidates. We took Engber's basic model, but automated the evaluation of lexical content. A group of non-native speakers of French were asked to pro- duce a short text in response to a picture stimulus. The texts were graded by French native speaker teachers. We identi®ed a number of words which occurred in about half the texts, and coded each text for the occurrence and non-occurrence of each word. We then trained a neural network to grade the texts on the basis of these codings. The results suggest that it might be possible to teach a neural network to mimic the judgements made by human mar- kers.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali