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- Word sense tagging is one of the most difficult problem in natural language processing. 词义标注是自然语言处理的难题之一。
- For the purpose of implementing automatic Chinese word sense tagging, this paper presents a new method for word sense disambiguation based on unsupervised machine learning strategies. 为实现汉语全文词义自动标注，本文采用了一种新的基于无指导机器学习策略的词义标注方法。
- Word Sense Tagging Method Based on Context 基于语境的语义排歧方法
- Word Sense Tagging in Chinese- English Parallel Corpora 汉英双语平行语料库的词义标注
- Word sense tagging method based on word relationships 一种基于词语搭配的语义消歧方法
- Implement a full- text automatic system for word sense tagging 一个全文词义自动标注系统的实现
- Unsupervised word sense tagging method based on transformation rules 基于转换的无指导词义标注方法
- Full-words Automatic Word Sense Tagging Based on Unsupervised Learning Algorithm 基于无指导机器学习的全文词义自动标注方法
- word sense tagging 词语义项标注
- Word Sense Disambiguation(WSD) and tagging 词义消歧与标注
- Abstract: Word sense disambiguation is a key problem and one of difficult points in natural language processing. 摘 要： 词义消歧一直是自然语言处理领域的关键问题和难点之一。
- The rate of correct word sense disambiguation is over 98 % in the closed test, and over 96% in the open test. 词义消歧的精确率，封闭测试高达98%25以上，开放测试高达96%25以上。
- Word sense disambiguation is one of the difficult problems in natural language processing. 摘要词义消歧是自然语言处理中的难题之一。
- The problem of word sense disambiguation can be formalized to be a typical classify problem. 摘要词义消歧问题可以形式化为典型的分类问题。
- Using pseudowords we can overcome data sparseness problem in supervised WSD and fully verify the experimental effect of word sense classifier. 使用伪词可以避免有指导的词义消歧方法中的数据稀疏问题，充分验证词义分类器的实验效果。
- As an important work in the field of Natural Language Processing, Word Sense Disambiguation (WSD) has been a research focus since 1950. 作为自然语言理解的一项基础工作，词义消歧(Word Sense Disambiguation, WSD)一直是研究的重点。
- I would like to have a word with you. 我想同你说句话。
- Word sense is one of the obstacles of natural language processing (NLP), and the factor which weakens the translation quality of machine translation (MT), also. 语义障碍是自然语言处理的一个拦路石，当然也是影响提高机器翻译译文质量的因素之一。
- 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。
- 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。
