一种高稳定性词汇共现模型

乔亚男; 齐勇; 侯迪

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一种高稳定性词汇共现模型

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- 一种高稳定性词汇共现模型
- A Highly Stable Term Co-Occurrence Model
- 西安交通大学学报 2009年43卷第6期页码：24-27
- 作者机构：
  
  西安交通大学计算机科学与技术系,西安,710049
- 作者简介：
- 基金信息：
  
  : 2008-09-05. 作者简介: 乔亚男(1979-),男,博士生;齐勇(联系人),男,教授,博士生导师. 基金项目: 国家高技术研究发展计划资助项目(2006AA01Z101);教育部高等学校博士学科点专项科研基金资助项目(20060698018)
- DOI：
  中图分类号： TP391
- 网络首发：2009-06-10，
  
  纸质出版：2009
- 稿件说明：
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乔亚男, 齐勇, 侯迪. 一种高稳定性词汇共现模型[J]. 西安交通大学学报, 2009,43(6):24-27.

A Highly Stable Term Co-Occurrence Model[J]. 2009, 43(6): 24-27.
乔亚男, 齐勇, 侯迪. 一种高稳定性词汇共现模型[J]. 西安交通大学学报, 2009,43(6):24-27. DOI：

A Highly Stable Term Co-Occurrence Model[J]. 2009, 43(6): 24-27. DOI：

摘要

针对传统词汇共现模型存在的缺乏理论基础和稳定性欠佳等问题

提出了一种基于项场的高稳定性词汇共现模型.借鉴经典物理学中场的概念给出了项场的定义

其中项是语言的基本单位

是概念的抽象描述

而项场则是项在文档中的影响范围.在此基础上

引入量子场论将项与项的相关度类比为项场的叠加

由此给出了项与项之间距离和相关度的函数关系

并用其建立了词汇共现模型.实验结果证明

在小距离的情况下

所提模型中项的相关度大体呈常数

具有一定的窗口内稳定性

而同范畴的项对相关度振幅只有对照模型中最小振幅的26%

表明它具有较好的数据集稳定性.

Abstract

To address the issues that traditional term co-occurrence models are lack of theoretical basis and poor stabile

a highly stable term co-occurrence model based on term field is proposed. The model uses the concept of field in classical physics for reference to define the term field(terms are the basic units of language

which describe the abstract concepts

and the term field is the area affected by a term in document). Based on the definition

the model regards correlation as a superposition of term fields

and gets the functional relations of terms correlation and the distances between terms. Experimental results show that the terms correlation in this model is almost a constant for small distances and stable enough in window. While the correlation amplitude of the terms in same category is only 26% of the best result obtained with other models

which means the model is stable enough in dataset.

关键词

Keywords

references

BEIGBEDER M, MERCIER A. An information retrieval model using the fuzzy proximity degree of term occurrences [C]∥Proceedings of the 2005 ACM Symposium on Applied Computing. New York, USA: ACM, 2005: 1018-1022.

PETKOVA D, CROFT W B. Proximity-based document representation for named entity retrieval[C]∥Proceedings of the 16th ACM Conference on Information and Knowledge Management. New York, USA: ACM, 2007: 731-740.

RASOLOFO Y, SAVOY J. Term proximity scoring for keyword-based retrieval systems [C]∥Proceedings of 25th European Conference on IR Research. Berlin, Germany: Springer, 2003: 207-218.

YAROWSKY A D. One sense per collocation[C]∥Proceedings of the ARPA Human Language Technology Workshop. Stroudsburg, PA, USA: Association for Computational Linguistics,1993: 266-271.

鲁松,白硕. 自然语言处理中词语上下文有效范围的定量描述 [J]. 计算机学报, 2001, 24(7): 742-747.

LU Song,BAI Shuo. Quantitative analysis of context field in natural language processing [J]. Chinese Journal of Computers, 2001,24(7): 742-747.

GAO Jianfeng, ZHOU Ming, NIE Jianyun, et al. Resolving query translation ambiguity using a decaying co-occurrence model and syntactic dependence relations [C]∥Proceedings of the 25th Annual International ACM SIGIR. New York, USA: ACM, 2002: 183-190

郭锋,李绍滋,周昌乐,等. 基于词汇吸引与排斥模型的共现词提取 [J]. 中文信息学报, 2004,18(6): 16-22.

GUO Feng,LI Shaozi,ZHOU Changle,et al. Co-occurrence word retrieval based on the lexical attraction and repulsion model [J]. Journal of Chinese Information Processing, 2004,18(6): 16-22.

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