Understanding temporal intents behind users' queries is essential to meet users' time-related information needs. In order to classify queries according to their temporal intent (e.g. Past or Future), we explore the usage of time-series data derived from Wikipedia page views as a feature source. While existing works leverage either proprietary search engine query logs or highly processed and aggregated data (such as Google Trends) for this purpose, we investigate the utility of a freely available data source for this purpose. Our experiments on the NTCIR-12 Temporalia-2 dataset show, that Wikipedia pageview-based time-series data can significantly improve the disambiguation of temporal intents for specific types of queries, in particular those without temporal expressions present in the query string.
Original languageEnglish
Title of host publicationProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Pages1017-1020
Number of pages4
ISBN (Print)978-1-4503-4069-4
StatePublished - 2016
Event39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016 - Pisa, Italy
Duration: 17 Jul 201621 Jul 2016

Conference

Conference39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016
CountryItaly
CityPisa
Period17/07/1621/07/16

    Research areas

  • Information systems, Information retrieval, Information retrieval query processing, Query intent

ID: 11370832