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词义消歧

网络Word Sense Disambiguation; WSD; Word sense disambiguation, WSD

词义消歧

词义消歧

词义消歧

Word Sense Disambiguation

? 词义消歧 (Word sense disambiguation)，WSD? 依存分析器 (Dependency Parser)? 基于局部动态算法的依存句法分析，Pars…

WSD

词义消歧(wsd)语义角色标注(SRL) @Windows @Linux 分词(CRFWordSeg) 词性标注(PosTag) 命名实体识别(NER) 依存句法 …

Word sense disambiguation, WSD

词义消歧（Word Sense Disambiguation, WSD）就是从给定上下文中确定一个多义词的具体意思（sense）。这项研究最早源 …

word sense discrimination

Hinrich Schütze 用词义消歧（ word sense discrimination）的WORDSPACE模型，对原来的方法，进行了很多早期工作。Stefa…

1

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。

2

Word sense disambiguation is one of the difficult problems in natural language processing .

词义消歧是自然语言处理中的难题之一。

3

The range of polysemous words as the object of the computer automatic word sense disambiguation is delimited .

先界定了作为计算机自动词义消歧对象的多义词的范围；

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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。

6

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

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。

8

Second, word alignment is very important to SMT, using WSD to improve the result of word alignment .

第二，词对齐对机器翻译起着很重要的作用，本文利用词义消歧改进词对齐。

9

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%以上，开放测试高达 96%以上。

10

ANN 's structure is relatively complex of its input for WSD.

神经网络的结构相对复杂，用于词义消歧需要先解决输入问题。

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

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。

13

In this paper, large scale real texts are applied in WSD based on two classical statistics model .

本文对两个经典的统计消歧模型在大规模的词义消歧上的应用效果进行了考察。

14

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。

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Coarse -Grained Word Sense Disambiguation Using Features Described in the Lexicon

基于词典属性特征的粗粒度词义消歧

16

The Research Progress of Statistical Word Sense Disambiguation

统计词义消歧的研究进展

17

The Model of Word Sense Disambiguation Combining Statistics and BP Neural Networks

统计与神经网络相结合的词义消歧模型

18

Word Sense Disambiguation with Semantic Graph Structure

语义网络结构下的词义消歧

20

The State-of-the-Art and Future Aspects of Word Sense Disambiguation

词义消歧研究的现状与发展方向

21

New Chinese Word Sense Disambiguation Method

一种新的汉语词义消歧方法

22

Word Sense Disambiguation Based on Dependency Relationship Analysis and Bayes Model

基于依存分析改进贝叶斯模型的词义消歧

23

Unsupervised approach to word sense disambiguation based on vector space model

基于向量空间模型的无导词义消歧

24

A model of Chinese word sense disambiguation based on combining rule and statistics method

规则与统计相结合的汉语词义消歧模型

25

Using the BP neural networks to Chinese word sense disambiguation

利用 BP 神经网络的中文词义消歧模型

26

Word Sense Disambiguation Based on Multi-Classifier Decision

基于多分类器决策的词义消歧方法

27

Chinese word sense disambiguation based on extension theory

基于可拓学理论的汉语词义消歧

28

An introduction of word sense disambiguation based on Bayers model

浅谈基于改进贝叶斯模型的词义消歧方法

29

Research on Unsupervised WSD Method Based on Sense Categories

基于义类的无导词义消歧方法的研究

30

Chinese WSD Based on Context Calculation Model

基于语境计算模型的汉语词义消歧

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