The Word Sense Disambiguation (WSD) study based on large scale real world corpus is performed using an unsupervised learning algorithm based on DGA improved Bayesian Model.
采用基于依存分析改进贝叶斯网络的无指导的机器学习方法对汉语大规模真实文本进行词义消歧实验。
Word sense disambiguation (WSD) plays an important role in many areas of natural language processing such as machine translation, information retrieval, sentence analysis, speech recognition.
词义排歧在机器翻译、信息检索、句子分析和语音识别等许多领域有重要的作用。
Word Sense Disambiguation (WSD) has always been a difficult and hot points in natural language processing.
词义消歧是自然然语言处理中的一个难点和热点问题。
This paper investigates their application effect on Chinese Word Sense Disambiguation (WSD) by experiments.
本文通过实验考察了这两种网络模型在汉语词义消歧上的应用效果。
WTD and its similar task - word sense disambiguation (WSD) in mono-lingual category are important and hard in the research of nature language processing (NLP) and are always the basis of it.
译文消歧及与之相似的在单语范畴内的词义消歧一直是自然语言处理领域基础研究课题，它也是自然语言处理技术的重点和难点之一。
WTD and its similar task - word sense disambiguation (WSD) in mono-lingual category are important and hard in the research of nature language processing (NLP) and are always the basis of it.
译文消歧及与之相似的在单语范畴内的词义消歧一直是自然语言处理领域基础研究课题，它也是自然语言处理技术的重点和难点之一。
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