Abstract
We propose a new method for extracting noun phrase correspondence automatically from a sentence-aligned bilingual corpus. In our approach, noun phrases extracted from each source language sentence are aligned to phrases in each target language sentence based on a phrase translation model and maximum translation probability. The method involves generating word level alignment using existing word alignment technique as the basis of noun phrase alignment, and estimating Lexical Translation Probability (LTP) for noun phrases by using the EM algorithm and estimating Fertility Probability (FP) from a Most Frequency Translation Equivalent (MFTE). At runtime, for each noun phrase in the source sentence, partial translation in the target sentence is located. Then, each of the n-grams containing the partial translation is evaluated using phrase translation probability. The n-gram with maximum translation probability is chosen as the output. We describe the implementation of the method using bilingual Hong Kong news corpus. The experimental results show that our model outperforms IBM model4 in terms of precision rate of noun phrase extraction. The methodology cleanly improves the performance of noun phrase translation, which has been shown to be very crucial for statistical machine translation.