• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

• 图形与图像 • 上一篇    下一篇

一种基于全局领域和短期记忆因子的图模型

邵玉涵,李培培,胡学钢   

  1. (合肥工业大学计算机与信息学院,安徽 合肥 230601)
  • 收稿日期:2018-11-15 修回日期:2019-03-26 出版日期:2019-10-25 发布日期:2019-10-25
  • 基金资助:

    国家自然科学基金(61673152)

A graph model based on global domain
 and short-term memory factor

SHAO Yu-han,LI Pei-pei,HU Xue-gang   

  1. (School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230601,China)
  • Received:2018-11-15 Revised:2019-03-26 Online:2019-10-25 Published:2019-10-25

摘要:

词义消歧是一项具有挑战性的自然语言处理难题。作为词义消歧中的一种优秀的半监督消歧算法,遗传蚁群词义消歧算法能快速进行全文词义消歧。该算法采用了一种局部上下文的图模型来表示语义关系,以此进行词义消歧。然而,在消歧过程中却丢失了全局语义信息,出现了消歧结果冲突的问题,导致算法精度降低。因此,
提出了一种基于全局领域和短期记忆因子改进的图模型来表示语义以解决这个问题。该图模型引入了全局领域信息,增强了图对全局语义信息的处理能力。同时根据人的短期记忆原理,在模型中引入了短期记忆因子,增强了语义间的线性关系,避免了消歧结果冲突对词义消歧的影响。大量实验结果表明:与经典词义消歧算法相比,所提的改进图模型提高了词义消歧的精度。

关键词: 词义消歧, 半监督消歧方法, 短期记忆模型, 全局领域信息

Abstract:

Word sense disambiguation (WSD) is a challenging problem in natural language processing. As an excellent semi-supervised disambiguation algorithm in WSD, the genetic max-minant system word sense disambiguation (GMMSWSD) can perform full-text WSD quickly. The algorithm uses a graph based on local context to represent semantic relationships for word sense disambiguation. However, in the process of disambiguation, global semantic information is lost and inconsistent  disambiguation results occur, which leads to lower accuracy of the algorithm. We therefore propose an improved graph model based on global domain and short-term memory factor to solve the abovementioned problems. The new graph model introduces global domain information to enhance the processing ability of global semantic information. At the same time, according to the principle of short-term memory, we introduce the short-term memory factor into the model, which can enhance the linear relationship between semantics and avoid the influence of inconsistent disambiguation results on word sense disambiguation. Experimental results show that compared with the classical word sense disambiguation algorithm, the proposal's precision of word sense disambiguation is improved.
 

Key words: word sense disambiguation (WSD), semi-supervised disambiguation method, short-term memory model, global domain information