关键词: 错误检测, 词后验概率, 语言学特征, 最大熵分类器

Abstract: The authors firstly introduce three typical word posterior probabilities (WPP) for error detection and classification, which are fixed position WPP, sliding window WPP, and alignment-based WPP, and analyzes their impact on the detection performance. Then each WPP feature is combined with three linguistic features (Word, POS and LG Parsing knowledge) over the maximum entropy classifier to predict the translation errors. Experimental results on Chinese-to-English NIST datasets show that the influences of different WPP features on the classification error rate (CER) are significant, and the combination of WPP with linguistic features can significantly reduce the CER and improve the prediction capability of the classifier.

Key words: error detection, word posterior probability, linguistic features, maximum entropy classifier