词义消歧

实验数据表明贝叶斯网络比神经网络更适合解决汉语词义消歧问题。

The experimental data shows that Bayesian network is fitter for solving the Chinese WSD than ANN .

因此，如何进一步改善词义消歧处理的效果，将是我们在词义消歧领域继续进行研究的动力和目标。

Thus the question how to improve the effect of WSD will be the motivation and objective of our further research in this field .

利用BP神经网络的中文词义消歧模型

Using the BP neural networks to Chinese word sense disambiguation

这篇文章提出了一种无导词义消歧的方法，该方法采用二阶context构造上下文向量，使用k-means算法进行聚类，最后通过计算相似度来进行词义的排歧。

This paper presents an unsupervised approach which constructs context vector by means of second-order context , clustering by k-means and disambiguates by calculating the similarity .

把多词表达作为词义消歧的一种知识资源，提出一种新的词义消歧方法，叫做多引擎协同自举（MCB）。

The author adopts a new word sense disambiguation method , called Multi-engine Collaborative Bootstrapping ( MCB ) and the collocation which is a kind of special MWEs is viewed as its knowledge resource .

在自然语言处理（NLP）中，词义消歧（WSD）一直是研究的重点和难点。

Word Sense Disambiguation ( WSD ) plays an important role in many areas of Natural Language Processing ( NLP ), and now it has become a hotspot and nodus . As an intermediate task , the research on WSD has great theoretical and practical significance in NLP .

现代汉语语义词典（SKCC）是一部面向中文信息处理的语义知识库，1998年底完成一期工程，收词48835条。汉英机器翻译中基于大型语义词典的汉语词义消歧

The Semantic Knowledge-base of Contemporary Chinese ( SKCC ) is a large machine-readable dictionary developed by the Institute of Computational Linguistics and Chinese Department of Peking University . A Study of Chinese Word Sense Disambiguation in MT Based on Semantic Knowledge-base

基于二阶上下文的无导词义消歧研究

An Unsupervised Approach to Word Sense Disambiguation Based on Second-order Context

神经网络和贝叶斯网络在汉语词义消歧上的对比研究

Chinese Word Sense Disambiguation : Neural Network vs. Bayesian Network

基于义类的无导词义消歧方法的研究

The Research on Unsupervised WSD Method Based on Sense Categories

基于条件随机场的古汉语词义消歧研究

The Ancient Chinese Word Sense Disambiguation Based on CRF

伪实例与人工标注实例相结合的词义消歧方法

Combining Pseudo-Samples and Manually-Tagged Samples for Word Sense Disambiguation

基于依存分析和贝叶斯网络的无指导汉语词义消歧

Unsupervised Chinese Word Sense Disambiguation Based on Dependency Grammar Analysis and Bayesian Network

基于向量空间模型的有导词义消歧

Supervised word sense disambiguation based on vector space model

基于汉语二元同现的统计词义消歧方法研究

Research on the Method of Word Sense Disambiguation Based on Target Language Bigram

中间词义消歧模块是本系统的核心，主要分为两大模块：相似度计算模块和相关度计算模块。

It is mainly divided into two parts : similarity calculation and relevance calculation .

统计词义消歧的研究进展

The Research Progress of Statistical Word Sense Disambiguation

中文词义消歧上下文最优边界问题研究

Optimal Context Window for Chinese Word Sense Disambiguation

基于统计的中文词义消歧技术研究

Research on Statistical Chinese Word Sense Disambiguation

基于最大熵原理的汉语词义消歧

Maximum Entropy-Based Chinese Word Sense Disambiguation

本文采用有监督方法进行词义消歧，使用多种机器学习方法从上下文中提取不同的信息来构建分类器。

We use a variety of machine learning methods extract different information to build many classifiers .

词义消歧与框架-脚本理论

Word Sense Disambiguation and Frame-Script Theory

在对10个典型多义词进行词义消歧的测试实验中，采用该方法取得了平均正确率为83.13%的消歧结果。

The average accuracy is 83.13 % for 10 polysemous words in open test by this method .

究其原因是因为仅仅使用关键词并没有明确地表达出用户查询的语义，即便是使用查询扩展和词义消歧的技术也不能完全解决这个问题。

It is mainly because merely using keywords is not sufficient for clearly describing the semantics in user queries .

词义消歧一直是自然语言处理中的重点和难点问题。

Word sense disambiguation ( WSD ) is all along an important and difficult problem in nature language processing .

第三，在未经词汇对齐的平行语料中，实践了基于个性规则的词性、词义消歧方法。

The POS and word sense disambiguation approaches based on individual rules have been experienced in non-lexical paralleled parallel corpus .

双语语料库在基于实例的机器翻译、翻译知识的获取、双语词典的建立、词义消歧等领域有着重要的应用价值。

Bilingual corpus plays an important role in Example-base Machine Translation ( EBMT ), acquirement of translation knowledge , construction of bilingual dictionary etc.

实验结果证明基于依存改进的贝叶斯模型在汉语词义消歧上表现良好，开放测试正确率可达86.27%。

Experimental results show that this approach does better on Chinese WSD , and the open test achieved an accuracy of 86.27 % .

第四章利用关联理论对消歧的解释力从意图识别、关联期盼和认知努力等方面对人脑的词义消歧机制进行论述。

Chapter Four provides a relevance theoretical account for human disambiguation from the aspects of intention recognition , relevance expectation and processing effort .

最后，进行了实例分析，验证了该方法在词义消歧中的有效性。

An illustrative example verifies the validity of this method in the word sense disambiguation . 4 . Application of FCA on semantic analysis .