An Improved Hierarchical Word Sequence Language Model Using Directional Information
Xiaoyi Wu, Yūji Matsumoto · Waseda University Repository (Waseda University) · 2015
For relieving data sparsity problem, Hierarchi-cal Word Sequence (abbreviated as HWS) lan-guage model, which uses word frequency in-formation to convert raw sentences into spe-cial n-gram sequences, can be viewed as an effective alternative to normal n-gram method. In this paper, we use directional information to make HWS models more syntactically ap-propriate so that higher performance can be achieved. For evaluation, we perform intrin-sic and extrinsic experiments, both verify the effectiveness of our improved model.