Despite a clear improvement of search and retrieval temporal applications, current search engines are still mostly unaware of the temporal dimension. Indeed, in most cases, systems are limited to offering the user the chance to restrict the search to a particular time period or to simply rely on an explicitly specified time span. If the user is not explicit in his/her search intents (e.g., “philip seymour hoffman”) search engines may likely fail to present an overall historic perspective of the topic. In most such cases, they are limited to retrieving the most recent results. One possible solution to this shortcoming is to understand the different time periods of the query. In this context, most state-of-the-art methodologies consider any occurrence of temporal expressions in web documents and other web data as equally relevant to an implicit time sensitive query. To approach this problem in a more adequate manner, we propose in this paper the detection of relevant temporal expressions to the query. Unlike previous metadata and query log-based approaches, we show how to achieve this goal based on information extracted from document content. However, instead of simply focusing on the detection of the most obvious date we are also interested in retrieving the set of dates that are relevant to the query. Towards this goal, we define a general similarity measure that makes use of co-occurrences of words and years based on corpus statistics and a classification methodology that is able to identify the set of top relevant dates for a given implicit time sensitive query, while filtering out the non-relevant ones. Through extensive experimental evaluation, we mean to demonstrate that our approach offers promising results in the field of temporal information retrieval (T-IR), as demonstrated by the experiments conducted over several baselines on web corpora collections.
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We understand non-relevant dates as temporal patterns which though being dates are not relevant to the query (e.g., avatar movie 2011) and wrong ones as those, which though being a temporal pattern do not form a data (e.g., 1500 photos).
i.e. that co-occur at least once with d j .
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This research was funded by Project "TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020" which is financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF) and by national founds of FCT-Foundation for Science and Technology under UID/MAT/00212/2013.
A short version of this work has appeared in Proceedings of TempWeb@WWW’12 (Campos et al. 2012).
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Campos, R., Dias, G., Jorge, A.M. et al. Identifying top relevant dates for implicit time sensitive queries. Inf Retrieval J 20, 363–398 (2017). https://doi.org/10.1007/s10791-017-9302-1
- Temporal information retrieval
- Implicit time sensitive queries
- Temporal query understanding
- Relevant temporal expressions