Semantic similarity between search engine queries using temporal correlation
WWW '05 Proceedings of the 14th international conference on World Wide Web
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Proceedings of the 15th international conference on World Wide Web
Towards the Detection of Breaking News from Online Web Search Keywords
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Gazpacho and summer rash: lexical relationships from temporal patterns of web search queries
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
An approach to use query-related web context on document ranking
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
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Many people enter queries to Google or Yahoo! in order to search useful information from the Web. Queries given to search engines can be regarded as the resources for detecting people's information needs. It is often reported that many people perform search intensively after worldwide disasters or accidents. This paper describes a method for detecting such breaking news from search queries that are available online. In our method, real time search queries are obtained and filtered with news words extracted From a news site. Experimental results show that our method has abilities of detecting breaking news from more than 25 million search queries for six months.