Structural Retrieval on Chinese Syntax Tree Corpus

MA Lu-yao; XIA Bo; XIAO Ye; XUN En-dong

doi:10.3969/j.issn.0372-2112.2020.05.001

您当前的位置：

首页 >

文章列表页 >

Structural Retrieval on Chinese Syntax Tree Corpus

更新时间：2025-07-16

- Structural Retrieval on Chinese Syntax Tree Corpus
- Acta Electronica Sinica Vol. 48, Issue 5, Pages: 833-839(2020)
- 作者机构：
  
  北京语言大学信息科学学院,北京,100083
- 作者简介：
- 基金信息：
  
  The National Social Science Fund of China (No.16AYY007);Research Program of Beijing Advanced Innovation Center for language Recourses (No.TYR17001J);Postgraduate Innovation Fund Project of Beijing Language and Culture University (No.19YCX118)
- DOI：10.3969/j.issn.0372-2112.2020.05.001
  CLC： TP391.12
- Published Online：25 May 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
MA Lu-yao, XIA Bo, XIAO Ye, et al. Structural Retrieval on Chinese Syntax Tree Corpus[J]. Acta Electronica Sinica, 2020, 48(5): 833-839.
DOI：

MA Lu-yao, XIA Bo, XIAO Ye, et al. Structural Retrieval on Chinese Syntax Tree Corpus[J]. Acta Electronica Sinica, 2020, 48(5): 833-839. DOI： 10.3969/j.issn.0372-2112.2020.05.001.

摘要

语言资源加工和语言学研究，对大规模树库的结构化检索有很高需求.本文针对句法树语料设计了索引、检索方法.针对汉语的特点以及知识抽取任务的需求，我们设计了七种索引结构，旨在借助句法树的结构、属性信息，进行高效、准确的知识抽取.本方法不仅支持字符串检索、属性检索，也支持基于句法树结构、属性信息的检索.实验证明，本方法高效、准确.

Abstract

Language resource processing and linguistics research require effective retrieval on syntax tree corpus. This paper presented an index and search method for syntax tree corpus

which is efficient

accurate

and flexible. Based on the features of Chinese language and the needs for knowledge extraction

we designed seven types of indexes

aiming that with the help of structure and attribute information

knowledge extraction will be performed more effectively and accurately. Apart from general retrieval functions

our method supports retrieval based on the structure and attribute information of syntax trees. Experiments show that our method is both accurate and efficient.

关键词

Keywords

references

Views

257

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Pseudo-Relevance Feedback Query Expansion Based on the Fusion of Association Pattern Mining and Word Embedding Learning

Efficiently Reducing Transferring Cost of Skyline Queries over Distributed Networks

The Least Upper Approximation of Concept

Predicting Query Performance Using Semantic Chart

Related Author

HUANG Ming-xuan

ZHU Peng-hua

WANG Jun-xing

LIU Zi-yang

XUE Pu

LIN Chen

XIANG Yang

Related Institution

School of Information and Statistics， Guangxi University of Finance and Economics

Guangxi Key Laboratory of Cross-border E-commerce Intelligent Information Processing， Guangxi University of Finance and Economics

Cangzhou Normal University

School of Computer Science, North China Institute of Aerospace Engineering

Department of Computer Science and EnginneeringSourtheast UniversityNanjingJiangsu 210096China

⁰