Using Hidden Markov Model for Text Information Extraction Based on Maximum Entropy

LIN Ya-ping; LIU Yun-zhong; ZHOU Shun-xian; CHEN Zhi-ping; CAI Li-jun

您当前的位置：

首页 >

文章列表页 >

Using Hidden Markov Model for Text Information Extraction Based on Maximum Entropy

更新时间：2025-07-16

- Using Hidden Markov Model for Text Information Extraction Based on Maximum Entropy
- Acta Electronica Sinica Vol. 33, Issue 2, Pages: 236-240(2005)
- 作者机构：
  
  湖南大学计算机与通信学院,湖南,长沙,410082
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391
- Published Online：25 February 2005，
  
  Published：2005
- 稿件说明：
移动端阅览
LIN Ya-ping, LIU Yun-zhong, ZHOU Shun-xian, et al. Using Hidden Markov Model for Text Information Extraction Based on Maximum Entropy[J]. Acta Electronica Sinica, 2005, 33(2): 236-240.
DOI：

LIN Ya-ping, LIU Yun-zhong, ZHOU Shun-xian, et al. Using Hidden Markov Model for Text Information Extraction Based on Maximum Entropy[J]. Acta Electronica Sinica, 2005, 33(2): 236-240. DOI：

摘要

文本信息抽取是处理海量文本的重要手段之一.最大熵模型提供了一种自然语言处理的方法.提出了一种基于最大熵的隐马尔可夫模型文本信息抽取算法.该算法结合最大熵模型在处理规则知识上的优势

以及隐马尔可夫模型在序列处理和统计学习上的技术基础

将每个观察文本单元所有特征的加权之和用来调整隐马尔可夫模型中的转移概率参数

实现文本信息抽取.实验结果表明

新的算法在精确度和召回率指标上比简单隐马尔可夫模型具有更好的性能.

Abstract

Text information extraction is an important approach to processing large quantity of text.Maximum entropy provides a kind of framework for natural language processing.A new algorithm using hidden Markov model based on maximal entropy is proposed for text information extraction.The new algorithm combines the advantage of maximum entropy model

which can integrate and process rules and knowledge efficiently

with that of hidden Markov model

which has powerful technique foundations to solve sequence representation and statistical problem

and uses the sum of all features with weights to adjust the transition parameters in hidden Markov model for text information extraction.Experimental results show that the new algorithm improves the performance in precision and recall.

关键词

Keywords

references

Views

3641

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Construction of Low-latency Artificial Intelligence of Things for Marine Meteorological Forecasting

Research Status of Computing-in-Memory Technology

A Survey: Computing Models of Artificial Intelligence Privacy Protection Based on Cryptographic Techniques

An Electromagnetic Signal Adversarial Examples Generation Method Based on Saliency Map

Regionalized Decision Algorithm for Human-Machine Shared Control Based on Gaussian Hidden Markov Model

Related Author

LI Jing-bo

MA Li

LI Yang

FU Ying-xun

MA Dong-chao

LI Jia-ning

YAO Peng

JIE Lu

Related Institution

School of Information Science and Technology, North China University of Technology

School of Computer Science, Beijing University of Technology

School of Integated Circuits， Tsinghua University

School of Computer Science and Engineering， Sun Yat-Sen University

Wuhan Digital Engineering Institute

⁰