This paper describes an empirical study of high-performance dependency parsers based on a semi-supervised learning approach. We describe an extension of semisupervised structured ...
Jun Suzuki, Hideki Isozaki, Xavier Carreras, Micha...
We propose a new semi-supervised model selection method that is derived by applying the structural risk minimization principle to a recent semi-supervised generalization error bou...
Many state-of-the-art statistical parsers for English can be viewed as Probabilistic Context-Free Grammars (PCFGs) acquired from treebanks consisting of phrase-structure trees enri...
As one of the important tasks of SemEval Evaluation, Frame Semantic Structure Extraction based on the FrameNet has received much more attention in NLP field. This task is often di...