Statistical machine translation ( smt ) is the text translation by the statistical parameter models obtained from the training corpus , which has become the mainstream of machine translation research 统计机器翻译是利用基于语料库训练得到的统计参数模型，将源语言的文本翻译成目标语言，它是机器翻译的主流方向。
Research on tagging of part - of - speech subcategory in modem chinese is the foundation for die research on nlp ( natural language . processing ) based on corpus , also a new project for further research 现代汉语词性细分类标注研究是当前基于语料库的自然语言处理研究的基础工作，这也是面向深层研究所遇到的新课题。
Chen keh - jiann and chen chao - jan discussed a computational approach in identifying unknown words . they tried to discover morphological rules for chinese by a corpus - based learning method . emphasis was put on the learning of rules that cannot be represented by regular expressions 陈克健和陈超然讨论了识别未登录词的计算方法，他们试图使用基于语料库的机器学习方法获取汉语的构词规律，并将学习的重点放在很难用“规则语法”来描述的构词方法。
We also introduce the main theories and techniques in the machine translation field nowadays , especially the corpus - based machine translation ( cbmt ) in ’ 90s , compare advantages and disadvantages between the new empirical approach and the old rational approach , and then we discuss the development trend and application prospects of machine translation 介绍了当今机器翻译领域的主要理论和方法,尤其是九十年代以来产生的新的基于语料库的方法,比较了新的经验主义的方法和传统的理性主义的方法的优缺点,讨论了机器翻译研究的发展趋势及其应用前景。
The building of corpus is the basic work in the area of chinese information processing . the processing of chinese corpus includes chinese word segmentation and part - of - speech tagging . they are widely used in many researches ( for example , the automatic searching of chinese text , machine translation , and chinese characters identification and so on ) , and they provide important study resources for these researches 自动分词和词性标注在很多现实应用(中文文本的自动检索、过滤、分类及摘要，中文文本的自动校对，汉外机器翻译，汉字识别与汉语语音识别的后处理，汉语语音合成，以句子为单位的汉字键盘输入，汉字简繁体转换等)中都扮演着关键角色，为众多基于语料库的研究提供重要的资源和有力的支持。
Upon this foundation , a corpus - based algorithm was designed and implemented to acquire and filter binary semantic pattern rules automatically . in the algorithm , a data mining method for cross - level association rules is adopted , which is guided by metarule , to find the semantic laws of word combinations in chinese phrase corpus . then statistic results are used to filter the findings 在此基础上，本文设计并实现了基于语料库的二元语义模式规则自动挖掘和优选算法，该算法先采用数据挖掘中元规则制导的交叉层关联规则挖掘方法，自动发现汉语短语熟语料库中词语两两组合的语义规律，再根据统计结果自动优选后转换生成候选二元语义模式规则集。
A semantic based disambiguation algorithm was designed and implemented . with the algorithm , word sense disambiguation and structure disambiguation can be done by semantic pattern rules matching during syntax parsing . the experiment result indicates that : ( a ) the presentation of semantic pattern rules can formalize the construction of chinese phrase quite well ; ( b ) the corpus - based algorithm for acquiring and filtering binary semantic pattern rules is effective , and it can reduce the human labor , avoid subjectivity and unilateralism caused by writing rules manually ; ( c ) the semantic based disambiguation algorithm can achieve satisfactory effects 实验表明： 1 )本文设计的语义模式规则能够较准确地刻画汉语短语构造的语义规律； 2 )本文提出的基于语料库的二元语义模式规则自动挖掘和优选算法是切实可行的，它大大减少完全由人工从大规模语料库中总结规则的工作量，避免了纯人工编制规则的主观性和片面性； 3 )本文提出的语义分析排歧算法能够有效消解短语分析中的词义歧义和结构歧义。
