A Hybrid Chinese Spelling Correction Using Language Model and Statistical Machine Translation with Reranking

Abstract

We describe the Nara Institute of Science and Technology (NAIST) spelling check system in the shared task. Our system contains three components: a word segmentation based language model to generate correction candidates; a statistical machine translation model to provide correction candidates and a Support Vector Machine (SVM) classifier to rerank the… (More)

Topics

5 Figures and Tables