Traditional research on spelling correction in natural language processing and information retrieval literature mostly relies on pre-defined lexicons to detect spelling errors. But this method does not work well for web query spelling correction, because there is no lexicon that can cover the vast amount of terms occurring across the web. Recent work showed that using search query logs helps to solve this problem to some extent. However, such approaches cannot deal with rarely-used query terms well due to the data sparseness problem. In this paper, a novel method is proposed for use of web search results to improve the existing query spelling correction models solely based on query logs by leveraging the rich information on the web related to the query and its top-ranked candidate. Experiments are performed based on realworld queries randomly sampled from search engine's daily logs, and the results show that our new method can achieve 16.9% relative F-measure improvement and 35.4% overall error rate reduction in comparison with the baseline method.
REF suggest using web search results to improve spelling correction.
Improving Query Spelling Correction Using Web Search Results
2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)
Computer Science