Rule-based And Statistical-based Combination Of Bilingual Parallel Sentence, The Phrase Alignment Method

Phrase alignment and is an important research field as Well as tough problem for machine translation and cross-language information retrieval. Phrases can be structural (i.e.base noun phrases) or non-structural.This thesis argues a model for Chinese-English phrase alignment.This model consists of three interconnected parts.The first is a word alignment module.Based this module, we can finish the alignment work,we align Chinese words and English words using approaches based on knowledge and EM algorithm. The Second part is a phrase identification module based on Marker Hypothesis and shallow parsing (mainly refers to base noun phrase identification).The last part is a phrase alignment module. As for word alignment, this thesis uses knowledge-based approach and EM algorithm based approach.The knowledge-based method uses bilingual dictionary as the main knowledge base.This simple approach is very efficient with high precision.It fails in the low recall.We uses synonym dictionary for Chinese word expansion to tackle the low recall. The latter approach uses EM algorithm to estimate the probability of a translation pair of words.In the stage of phrase alignment, we firstly design an algorithm for 1-to-n phrase alignment using word alignment result. As for the Chinese phrases which can't find corresponding English phrases using this algorithm,we will try to find the candidate English phrases for each Chinese phrase and rank the candidates using maximum entropy framework.Finally many-to-many phrase alignment can be acquired from the one-to-many phrase alignment results.