IEICE TRANSACTIONS on Information
Error Correction for Search Engine by Mining Bad Case
Summary :
Automatic error correction of users' search terms for search engines is an important aspect of improving search engine retrieval efficiency, accuracy and user experience. In the era of big data, we can analyze and mine massive search engine logs to release the hidden mind with big data ideas. It can obtain better results through statistical modeling of query errors in search engine log data. But when we cannot find the error query in the log, we can't make good use of the information in the log to correct the query result. These undiscovered error queries are called Bad Case. This paper combines the error correction algorithm model and search engine query log mining analysis. First, we explored Bad Cases in the query error correction process through the search engine query logs. Then we quantified the characteristics of these Bad Cases and built a model to allow search engines to automatically mine Bad Cases with these features. Finally, we applied Bad Cases to the N-gram error correction algorithm model to check the impact of Bad Case mining on error correction. The experimental results show that the error correction based on Bad Case mining makes the precision rate and recall rate of the automatic error correction improved obviously. Users experience is improved and the interaction becomes more friendly.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.7 pp.1938-1945
- Publication Date
- 2018/07/01
- Publicized
- 2018/03/26
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDP7284
- Type of Manuscript
- PAPER
- Category
- Natural Language Processing
Authors
Jianyong DUAN
North China University of Technology,Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data
Tianxiao JI
North China University of Technology,Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data
Hao WANG
North China University of Technology,Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data
Keyword
Latest Issue
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Jianyong DUAN, Tianxiao JI, Hao WANG, "Error Correction for Search Engine by Mining Bad Case" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 7, pp. 1938-1945, July 2018, doi: 10.1587/transinf.2017EDP7284.
Abstract: Automatic error correction of users' search terms for search engines is an important aspect of improving search engine retrieval efficiency, accuracy and user experience. In the era of big data, we can analyze and mine massive search engine logs to release the hidden mind with big data ideas. It can obtain better results through statistical modeling of query errors in search engine log data. But when we cannot find the error query in the log, we can't make good use of the information in the log to correct the query result. These undiscovered error queries are called Bad Case. This paper combines the error correction algorithm model and search engine query log mining analysis. First, we explored Bad Cases in the query error correction process through the search engine query logs. Then we quantified the characteristics of these Bad Cases and built a model to allow search engines to automatically mine Bad Cases with these features. Finally, we applied Bad Cases to the N-gram error correction algorithm model to check the impact of Bad Case mining on error correction. The experimental results show that the error correction based on Bad Case mining makes the precision rate and recall rate of the automatic error correction improved obviously. Users experience is improved and the interaction becomes more friendly.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7284/_p
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@ARTICLE{e101-d_7_1938,
author={Jianyong DUAN, Tianxiao JI, Hao WANG, },
journal={IEICE TRANSACTIONS on Information},
title={Error Correction for Search Engine by Mining Bad Case},
year={2018},
volume={E101-D},
number={7},
pages={1938-1945},
abstract={Automatic error correction of users' search terms for search engines is an important aspect of improving search engine retrieval efficiency, accuracy and user experience. In the era of big data, we can analyze and mine massive search engine logs to release the hidden mind with big data ideas. It can obtain better results through statistical modeling of query errors in search engine log data. But when we cannot find the error query in the log, we can't make good use of the information in the log to correct the query result. These undiscovered error queries are called Bad Case. This paper combines the error correction algorithm model and search engine query log mining analysis. First, we explored Bad Cases in the query error correction process through the search engine query logs. Then we quantified the characteristics of these Bad Cases and built a model to allow search engines to automatically mine Bad Cases with these features. Finally, we applied Bad Cases to the N-gram error correction algorithm model to check the impact of Bad Case mining on error correction. The experimental results show that the error correction based on Bad Case mining makes the precision rate and recall rate of the automatic error correction improved obviously. Users experience is improved and the interaction becomes more friendly.},
keywords={},
doi={10.1587/transinf.2017EDP7284},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Error Correction for Search Engine by Mining Bad Case
T2 - IEICE TRANSACTIONS on Information
SP - 1938
EP - 1945
AU - Jianyong DUAN
AU - Tianxiao JI
AU - Hao WANG
PY - 2018
DO - 10.1587/transinf.2017EDP7284
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2018
AB - Automatic error correction of users' search terms for search engines is an important aspect of improving search engine retrieval efficiency, accuracy and user experience. In the era of big data, we can analyze and mine massive search engine logs to release the hidden mind with big data ideas. It can obtain better results through statistical modeling of query errors in search engine log data. But when we cannot find the error query in the log, we can't make good use of the information in the log to correct the query result. These undiscovered error queries are called Bad Case. This paper combines the error correction algorithm model and search engine query log mining analysis. First, we explored Bad Cases in the query error correction process through the search engine query logs. Then we quantified the characteristics of these Bad Cases and built a model to allow search engines to automatically mine Bad Cases with these features. Finally, we applied Bad Cases to the N-gram error correction algorithm model to check the impact of Bad Case mining on error correction. The experimental results show that the error correction based on Bad Case mining makes the precision rate and recall rate of the automatic error correction improved obviously. Users experience is improved and the interaction becomes more friendly.
ER -
English
