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数据分析与知识发现  2021, Vol. 5 Issue (6): 115-125     https://doi.org/10.11925/infotech.2096-3467.2020.1312
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于词嵌入与扩展词交集的查询扩展*
黄名选1,2(),蒋曹清1,2,卢守东2
1广西财经学院广西跨境电商智能信息处理重点实验室 南宁 530003
2广西财经学院信息与统计学院 南宁 530003
Expanding Queries Based on Word Embedding and Expansion Terms
Huang Mingxuan1,2(),Jiang Caoqing1,2,Lu Shoudong2
1Guangxi Key Laboratory of Cross-border E-commerce Intelligent Information Processing, Guangxi University of Finance and Economics, Nanning 530003, China
2School of Information and Statistics, Guangxi University of Finance and Economics, Nanning 530003, China
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摘要 

【目的】 针对信息检索中词不匹配问题,提出一种词嵌入与扩展词交集融合的查询扩展模型。【方法】 对初检文档集进行词嵌入学习训练和关联规则挖掘,分别得到词嵌入候选扩展词集和挖掘候选扩展词集,将这两种候选扩展词集进行交集融合得到最终扩展词集,实现查询扩展。【结果】 实验结果表明,所提扩展模型检索结果MAP和P@5高于基准检索,与近年同类查询扩展方法比较,其MAP和P@5平均增幅范围分别为0.96%~31.24%和1.07%~13.55%。【局限】 只进行实验性研究,需要继续探讨在实际信息检索系统中的具体应用。【结论】 所提模型能提高扩展词质量,改善检索性能,遏制查询主题漂移和词不匹配问题。

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黄名选
蒋曹清
卢守东
关键词 信息检索查询扩展文本挖掘深度学习词嵌入    
Abstract

[Objective] This paper proposes a query expansion model based on the intersection of word embedding and expansion terms, aiming to reduce the mismatched words in information retrieval. [Methods] First, we trained the word embedding learning with the retrieved documents to obtain the Word Embedding Candidate Expansion Term set. Then, we examined the association rules and generated the Mining Candidate Expansion Term set. Finally, we created the final expansion term set by merging the previous two sets and expanded the queries. [Results] The MAP and P@5 of the proposed model were higher than those of the benchmark ones. Compared with the similar query expansion methods developed in recent years, the average increase of the MAP and P@5 were 0.96%-31.24% and 1.07%-13.55%, respectively. [Limitations] The proposed model needs to be examined with real world information retrieval systems. [Conclusions] The proposed model can improve the quality of expansion terms and the performance of information retrieval systems, which also reduces query topic drifting and word mismatch issues.

Key wordsInformation Retrieval    Query Expansion    Text Mining    Deep Learning    Word Embedding
收稿日期: 2020-12-30      出版日期: 2021-07-06
ZTFLH:  TP393  
基金资助:*国家自然科学基金项目(61762006)
通讯作者: 黄名选     E-mail: mingxh05@163.com
引用本文:   
黄名选,蒋曹清,卢守东. 基于词嵌入与扩展词交集的查询扩展*[J]. 数据分析与知识发现, 2021, 5(6): 115-125.
Huang Mingxuan,Jiang Caoqing,Lu Shoudong. Expanding Queries Based on Word Embedding and Expansion Terms. Data Analysis and Knowledge Discovery, 2021, 5(6): 115-125.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.1312      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2021/V5/I6/115
Fig.1  本文查询扩展模型
语料集 简称 文档数 语料集 简称 文档数
edn2000 EN00 79 380 mhn2000 MN00 84 437
end2001 EN01 93 467 mhn2001 MN01 85 302
ude2000 UE00 40 445 udn2000 UN00 244 038
ude2001 UE01 51 851 udn2001 UN01 222 526
Table 1  原始语料集及文档数量
对比查询扩展方法 描述
ARG_QE (Query Expansion Based on Association Rules Graph) 文献[9]基于规则图的查询扩展方法。实验参数:ms∈(0.1,0.12,0.13,0.14,0.15),mc=0.3, minqEn=10,minEEn=2, Lift=0.1, Jaccard=0.45, IG=0.2
WAP_QE (Query Expansion Based on Weighted Association Patterns) 基于文献[10]加权关联模式挖掘技术的查询扩展方法。实验参数:ms∈(0.004,0.005,0.006,0.007) , mc=0.1,mi=0.0001
WPNP_QE (Query Expansion Based on Weighted Positive and Negative Patterns) 基于文献[13]完全加权正负关联模式挖掘技术的查询扩展方法。实验参数:ms∈(0.10,0.11,0.12,0.13), mc=0.1, α=0.3, minPR=0.1,minNR=0.01
WMS_QE (Query Expansion Based on Weighted Multiple Supports) 基于文献[15]多支持度阈值的加权频繁模式挖掘技术的查询扩展方法。实验参数:ms∈(0.1,0.15,0.2,0.25), mc=0.1, LMS=0.2, HMS=0.25, WT=0.1
WE_QE (Query Expansion Based on Word Embedding) 采用文献[21]基于词向量的查询扩展方法(详见文献中方法1),按文献[21]公式(9)计算扩展词权值
Table 2  对比方法
Fig.2  本文扩展模型不同ms值的检索结果
Fig.3  本文扩展模型不同mc值的检索结果
评价 扩展方法 EN01 EN00 UE01 UE00 MN01 MN00 UN01 UN00 平均增幅/%
Relax BLR 0.199 2 0.427 8 0.249 7 0.370 1 0.314 4 0.304 9 0.267 9 0.218 0 27.59
WE_QE 0.230 1 0.461 5 0.284 2 0.437 5 0.378 3 0.326 4 0.299 3 0.283 1 10.49
WAP_QE 0.255 1 0.477 7 0.269 4 0.413 0 0.351 7 0.344 7 0.296 3 0.277 7 10.92
WMS_QE 0.201 0 0.463 1 0.281 5 0.462 2 0.337 0 0.348 1 0.271 4 0.269 9 14.67
WPNP_QE 0.212 2 0.462 8 0.300 6 0.500 3 0.333 2 0.287 1 0.312 2 0.271 3 12.51
ARG_QE 0.221 7 0.456 0 0.282 7 0.468 0 0.350 2 0.295 9 0.294 9 0.289 1 12.62
WEL&ETM_QE 0.270 9 0.470 5 0.318 9 0.545 1 0.377 4 0.358 2 0.343 7 0.291 9
Rigid BLR 0.120 0 0.281 4 0.179 5 0.207 5 0.185 0 0.208 9 0.183 9 0.125 3 27.87
WE_QE 0.131 0 0.314 5 0.178 7 0.253 1 0.214 2 0.198 7 0.190 6 0.159 6 16.15
WAP_QE 0.169 0 0.331 3 0.166 1 0.216 5 0.197 6 0.201 6 0.195 4 0.159 7 15.71
WMS_QE 0.139 8 0.335 9 0.166 8 0.245 2 0.185 6 0.217 2 0.177 6 0.149 6 18.21
WPNP_QE 0.138 3 0.321 8 0.199 7 0.305 6 0.190 1 0.189 4 0.205 4 0.147 4 12.84
ARG_QE 0.151 0 0.332 6 0.180 4 0.230 6 0.196 6 0.177 1 0.180 6 0.166 5 17.78
WEL&ETM_QE 0.185 1 0.325 9 0.208 4 0.324 6 0.198 8 0.246 5 0.229 9 0.162 7
Table 3  各个扩展方法Title查询的检索结果MAP值
评价 扩展方法 EN01 EN00 UE01 UE00 MN01 MN00 UN01 UN00 平均增幅/%
Relax BLR 0.203 9 0.384 8 0.323 9 0.384 8 0.323 7 0.286 2 0.288 9 0.228 0 22.47
WE_QE 0.209 7 0.384 0 0.340 5 0.384 0 0.416 4 0.341 4 0.312 8 0.312 6 10.09
WAP_QE 0.245 7 0.403 6 0.345 6 0.403 6 0.380 9 0.356 5 0.335 3 0.326 3 10.09
WMS_QE 0.220 3 0.434 0 0.311 8 0.434 0 0.364 1 0.348 2 0.328 4 0.283 2 9.00
WPNP_QE 0.251 4 0.478 8 0.379 4 0.478 8 0.347 6 0.329 4 0.383 8 0.288 9 0.96
ARG_QE 0.207 6 0.295 9 0.310 7 0.295 9 0.365 3 0.284 9 0.279 3 0.279 9 27.88
WEL&ETM_QE 0.255 5 0.469 6 0.378 0 0.469 6 0.361 9 0.341 6 0.370 0 0.306 5
Rigid BLR 0.110 3 0.278 2 0.173 1 0.278 2 0.176 9 0.189 5 0.196 5 0.143 9 23.21
WE_QE 0.113 9 0.269 0 0.177 3 0.269 0 0.234 6 0.215 7 0.197 4 0.199 9 13.73
WAP_QE 0.137 5 0.272 7 0.191 0 0.272 7 0.208 2 0.219 9 0.227 8 0.193 5 8.72
WMS_QE 0.120 4 0.303 0 0.181 6 0.303 0 0.200 1 0.211 7 0.225 0 0.166 9 11.21
WPNP_QE 0.134 9 0.315 6 0.219 8 0.315 6 0.184 3 0.231 2 0.256 1 0.172 0 3.67
ARG_QE 0.110 9 0.223 6 0.171 9 0.223 6 0.198 1 0.182 1 0.158 3 0.171 9 31.24
WEL&ETM_QE 0.147 9 0.338 9 0.209 3 0.338 9 0.192 0 0.241 1 0.252 8 0.176 2
Table 4  各个扩展方法Desc查询的检索结果MAP值
评价 扩展方法 EN01 EN00 UE01 UE00 MN01 MN00 UN01 UN00 平均增幅/%
Relax BLR 0.200 0 0.325 0 0.193 1 0.206 9 0.358 8 0.316 7 0.336 4 0.260 0 20.41
WE_QE 0.191 4 0.339 6 0.210 3 0.260 3 0.450 0 0.309 7 0.355 7 0.341 1 7.98
WAP_QE 0.243 1 0.358 3 0.232 8 0.265 5 0.413 2 0.325 0 0.352 3 0.331 1 3.74
WMS_QE 0.194 8 0.333 3 0.222 4 0.289 7 0.394 1 0.340 3 0.312 5 0.328 9 8.65
WPNP_QE 0.165 5 0.333 3 0.200 0 0.289 7 0.376 5 0.300 0 0.368 2 0.337 8 12.21
ARG_QE 0.191 7 0.338 3 0.245 5 0.306 2 0.410 6 0.273 3 0.380 0 0.342 2 6.14
WEL&ETM_QE 0.206 9 0.358 3 0.234 5 0.289 7 0.447 1 0.361 1 0.390 9 0.346 7
Rigid BLR 0.137 9 0.208 3 0.158 6 0.137 9 0.264 7 0.244 4 0.263 6 0.220 0 17.46
WE_QE 0.113 8 0.245 8 0.162 1 0.155 2 0.310 3 0.229 2 0.270 5 0.276 7 11.57
WAP_QE 0.174 1 0.237 5 0.184 5 0.167 2 0.269 1 0.225 0 0.273 9 0.265 6 5.65
WMS_QE 0.134 5 0.233 3 0.172 4 0.179 3 0.276 5 0.250 0 0.252 3 0.255 6 8.91
WPNP_QE 0.103 4 0.225 0 0.158 6 0.193 1 0.270 6 0.222 2 0.300 0 0.253 3 13.55
ARG_QE 0.143 4 0.235 0 0.195 9 0.189 0 0.287 1 0.192 2 0.296 4 0.266 7 6.30
WEL&ETM_QE 0.151 7 0.225 0 0.200 0 0.186 2 0.294 1 0.272 2 0.313 6 0.262 2
Table 5  各个扩展方法Title查询的检索结果P@5值
评价 扩展方法 EN01 EN00 UE01 UE00 MN01 MN00 UN01 UN00 平均增幅/%
Relax BLR 0.248 3 0.333 3 0.234 5 0.234 5 0.364 7 0.266 7 0.368 2 0.244 4 14.95
WE_QE 0.244 8 0.354 2 0.241 4 0.256 9 0.451 5 0.302 8 0.337 5 0.318 9 5.82
WAP_QE 0.265 5 0.366 7 0.248 3 0.265 5 0.422 1 0.316 7 0.393 2 0.291 1 2.70
WMS_QE 0.250 0 0.306 3 0.262 1 0.265 5 0.398 5 0.326 4 0.368 2 0.280 0 6.90
WPNP_QE 0.248 3 0.350 0 0.269 0 0.275 9 0.429 4 0.333 3 0.418 2 0.275 6 1.70
ARG_QE 0.238 6 0.366 7 0.235 9 0.229 0 0.418 8 0.316 7 0.353 6 0.270 2 9.35
WEL&ETM_QE 0.282 8 0.350 0 0.282 8 0.255 2 0.452 9 0.322 2 0.404 5 0.284 4
Rigid BLR 0.172 4 0.191 7 0.165 5 0.172 4 0.247 1 0.183 3 0.304 5 0.320 0 17.13
WE_QE 0.155 2 0.222 9 0.170 7 0.198 3 0.316 2 0.216 7 0.271 6 0.391 1 7.54
WAP_QE 0.186 2 0.233 3 0.163 8 0.191 4 0.292 6 0.229 2 0.314 8 0.376 7 4.01
WMS_QE 0.167 2 0.206 3 0.191 4 0.196 6 0.276 5 0.231 9 0.285 2 0.356 7 7.18
WPNP_QE 0.186 2 0.208 3 0.206 9 0.193 1 0.282 4 0.244 4 0.336 4 0.373 3 1.07
ARG_QE 0.162 8 0.233 3 0.173 8 0.180 7 0.277 6 0.221 1 0.260 0 0.355 6 10.42
WEL&ETM_QE 0.200 0 0.216 7 0.200 0 0.186 2 0.311 8 0.238 9 0.322 7 0.373 3
Table 6  各个扩展方法Desc查询的检索结果P@5值
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