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。
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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