The research on the bilingual dictionary extraction based on parallel corpora is an important direction.
基于平行语料抽取双语词典是一个很重要的研究方向。
Word alignment is a basic problem of Cross-lingual Natural Language Processing. Many NLP tasks based on bilingual corpus such as SBMT, EBMT, WSD, Automated Dictionary Extraction need to align words.
词语对齐是跨语言自然语言处理领域的一个基本问题，许多基于双语语料库的应用(如sbmt、EBMT、WSD、词典编纂)都需要词汇级别的对齐。
You can use built-in text analysis functions, namely dictionary based and regular expression based named entity extraction, as explained in the previous articles of this series.
您可以使用内置的文本分析特性，即基于词典和基于正则表达式的命名实体提取，如本系列的前面的文章所述。
With the help of a dictionary editor, you can first build a dictionary and then use the dictionary lookup operator to embed the concept extraction in a flow.
借助字典编辑器的帮助，您首先可以构建一个字典，然后使用字典查找操作符来将概念提取嵌入到流中。
Frequent Terms Extraction is an important feature for the efficient creation of dictionaries that can be used in dictionary-based analysis.
FrequentTermsExtraction是一个重要的特性，有助于高效地创建在基于词典的分析中使用的词典。
The second method is pattern matching algorithm based on the patterns of dictionary definition, we form some extraction rules by hands, the system then automatic extract synonyms by pattern matching.
第二部分是利用词汇定义模式，对词汇的释义方式进行分析，归纳总结出在词典释义中同义词出现的模式，进而利用模式匹配方法获取同义词。
Targeting at extending the dictionary for word segmentation so as to improve its accuracy, this paper presents a high-frequency Chinese word extraction algorithm based on information entropy.
为扩展分词词典，提高分词的准确率，本文提出了一种基于信息熵的中文高频词抽取算法，其结果可以用来识别未登录词并扩充现有词典。
By clustering extraction patterns are divided into different clusters and then according to different clusters the different attribute values are extracted from the dictionary.
通过对抽取模式进行聚类并按内涵属性类型划分为不同的簇，再按照不同的簇从词典中抽取出不同内涵属性类型的内涵属性值。
By clustering extraction patterns are divided into different clusters and then according to different clusters the different attribute values are extracted from the dictionary.
通过对抽取模式进行聚类并按内涵属性类型划分为不同的簇，再按照不同的簇从词典中抽取出不同内涵属性类型的内涵属性值。
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