数字图书馆论坛Issue(7)：34-39,6.DOI:10.3772/j.issn.1673-2286.2016.7.006
一种基于最大匹配和向量空间模型的用户检索词规范化方法
An Approach for Normalizing Retrieval Word Based on Maximum Matching and Vector Space Model
摘要
Abstract
It can conduct much more or a lit le result using free terms as retrieval word. Existing research results show that it can improve the recal and precision of a retrieval system using normalized terms from control ed vocabularies. In this paper, we propose a new approach to normalize retrieval words base on maximum matching algorithm and vector space model, which deal with the retrieval words in the two aspects of morphology and semantics. This method first exploits maximum matching to normalize the retrieval words from morphology and obtain candidate words, then respectively construct the vector of the candidate word and the retrieval word to compute semantic similarity, and final y selected the most similar candidate word as the normalized word of the retrieval word. The experimental results showed that the proposed method obtained a promising result, with the precision of more than 90%on the condition that retrieval word is a single word.关键词
最大匹配/向量空间模型/规范化/叙词表Key words
Maximum Matching/VSM/Normalization/Thesaurus分类
社会科学引用本文复制引用
何伟,常春..一种基于最大匹配和向量空间模型的用户检索词规范化方法[J].数字图书馆论坛,2016,(7):34-39,6.基金项目
本研究得到中国博士后科学基金项目“基于叙词表语义关系的智能检索模型研究”（编号2014M550791）资助。 （）
