Learning to order words: A connectionist model of heavy NP shift and accessibility effects in Japanese and English

  Chang, F. (2009). Learning to order words: A connectionist model of heavy NP shift and accessibility effects in Japanese and English. Journal of Memory and Language, 61(3), 374–397      pdf.

Languages differ from one another and must therefore be learned. Processing biases in

word order can also differ across languages. For example, heavy noun phrases tend to be shifted

to late sentence positions in English, but to early positions in Japanese. Although these language

differences suggest a role for learning, most accounts of these biases have focused on processing

factors. This paper presents a learning-based account of these word order biases in the form of a

connectionist model of syntax acquisition that can learn the distinct grammatical properties of

English and Japanese while, at the same time, accounting for the cross-linguistic variability in

processing biases in sentence production. This account demonstrates that the incremental nature

of sentence processing can have an important effect on the representations that are learned in

different languages.

The models run in LENS, so you will need to install that first.

The statistics are done with R, so that also needs to be installed.

The ejmodel2.tar.gz provides the data from the final set of models and an R script to produce the graphs.

You can also run a full set of models using the scripts in the archive.

9/2010: updated a few scripts in distribution -> ejmodel2.tar.gz