Word sense disambiguation has always been important in natural language processing.
词义排歧在自然语言处理领域占有重要地位。
A word sense disambiguation method based on semantic graph structure was presented.
提出了一种基于语义网络结构的词义消歧方法。
The problem of word sense disambiguation can be formalized to be a typical classify problem.
词义消歧问题可以形式化为典型的分类问题。
First of all, the significance of unsupervised word sense disambiguation study is introduced.
首先，介绍了无监督词义消歧研究的意义。
The precision rate of word sense disambiguation depends on the completeness of disambiguation knowledge.
词义排歧的精确率依赖于排歧知识的完备性。
Word sense disambiguation is one of the difficult problems and a key point in natural language processing.
词义排岐是自然语言处理的重点和难点问题之一。
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。
Lots of semantic rules are obtained from the experiments, and are used to sense disambiguation. The results are satisfactory.
经过实验，得到大量语义规则，并用其进行义项消歧实验，取得了较好的结果。
The goal of this paper is to give a brief summary of the current unsupervised word sense disambiguation techniques in order to facilitate future research.
研究的目的是对现有的无监督词义消歧技术进行总结，以期为进一步的研究指明方向。
The results of this study indicate that the SKCC is effective for word sense disambiguation in MT system and are likely to be important for general Chinese NLP.
初步的实验结果表明，该方法可以有效地进行汉语名词、动词、形容词的词义消歧。
Only some ambiguous words are disambiguated objects in many word sense disambiguation researches at present. There practices have limitation in real application.
目前进行的很多词义消歧研究多采用凡个多义词作为试验测试对象，在实际应用方面存在着局限性。
Parallel corpus has valuable application in machine translation, bilingual dictionary compilation, word sense disambiguation and Cross-Lingual Information Retrieval.
除机器翻译方面的应用之外，平行语料库的建设对于双语词典编纂、词义消岐和跨语言信息检索也具有重要价值。
Moreover , the parallel corpus is valuable in machine translation , bilingual dictionary compilation , word sense disambiguation and cross - lingual information retrieval.
除机器翻译方面的应用之外，平行语料库的建设对于双语词典编纂、词义消岐和跨语言信息检索也具有重要价值。
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。
The former includes Chinese word segmentation, part-of-speech tagging, pinyin tagging, named entity recognition, new word detection, syntactic parsing, word sense disambiguation, etc .
前者涉及到词法、句法、语义分析，包括汉语分词、词性标注、注音、命名实体识别、新词发现、句法分析、词义消歧等。
The former includes Chinese word segmentation, part - of - speech tagging, pinyin tagging, named entity recognition, new word detection, syntactic parsing, word sense disambiguation, etc.
前者涉及到词法、句法、语义分析，包括汉语分词、词性标注、注音、命名实体识别、新词发现、句法分析、词义消歧等。
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。
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) 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。
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