Use of Temporal Expressions in Web Search

Nunes, Sérgio; Ribeiro, Cristina; David, Gabriel

doi:10.1007/978-3-540-78646-7_59

Cited by 53 publications

(31 citation statements)

References 6 publications

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“…Same as in real life, we should expect many search activities to be of a strong temporal character. Previous studies (Joho, Jatowt, & Roi, 2013;Nunes, Ribeiro, & David, 2008) have confirmed this fact and elucidated a relatively high number of search intents and queries that center on information associated to particular temporal scopes such as future or past. Regarding future-oriented search, users often need to know more about planned events, forecasted trends, possible scenarios, speculations, predictions and so on.…”

Section: Introductionmentioning

confidence: 54%

“…Temporal Information Retrieval (e.g., Alonso, Baeza-Yates, Strötgen, & Gertz, 2011;Berberich et al, 2010;Campos, Dias, & Jorge, 2011;Campos, Dias, Jorge, & Jatowt, 2014;Campos et al, 2012;Joho, Jatowt, & Blanco, 2014a;Joho et al, 2013;Jones & Diaz, 2007;Kanhabua & Nørvåg, 2010;Mazur, 2012;Metzler et al, 2009;Nunes et al, 2008) has increasingly been gaining much interest in the IR community. This subarea of information retrieval focuses on temporal aspects of search, treating time as crucial facet for determining document relevance.…”

Section: Related Workmentioning

confidence: 98%

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Temporal information searching behaviour and strategies

Joho

Jatowt

Blanco

2015

Information Processing & Management

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a b s t r a c tTemporal aspects have been receiving a great deal of interest in Information Retrieval and related fields. Although previous studies have proposed, designed and implemented temporal-aware systems and solutions, understanding of people's temporal information searching behaviour is still limited. This paper reports the findings of a user study that explored temporal information searching behaviour and strategies in a laboratory setting. Information needs were grouped into three temporal classes (Past, Recency, and Future) to systematically study their characteristics. The main findings of our experiment are as follows. (1) It is intuitive for people to augment topical keywords with temporal expressions such as history, recent, or future as a tactic of temporal search. (2) However, such queries produce mixed results and the success of query reformulations appears to depend on topics to a large extent. (3) Search engine interfaces should detect temporal information needs to trigger the display of temporal search options. (4) Finding a relevant Wikipedia page or similar summary page is a popular starting point of past information needs. (5) Current search engines do a good job for information needs related to recent events, but more work is needed for past and future tasks. (6) Participants found it most difficult to find future information. Searching for domain experts was a key tactic in Future search, and file types of relevant documents are different from other temporal classes. Overall, the comparison of search across temporal classes indicated that Future search was the most difficult and the least successful followed by the search for the Past and then for Recency information. This paper discusses the implications of these findings on the design of future temporal IR systems.

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Section: Introductionmentioning

confidence: 54%

Section: Related Workmentioning

confidence: 98%

Temporal information searching behaviour and strategies

Joho

Jatowt

Blanco

2015

Information Processing & Management

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“…More precisely, documents are about events that happened at a particular time period, and also accesses to the contents are time-sensitive, i.e., time is part of information needs as represented by temporal queries (e.g., Illinois earthquake 1968 or Iraq 2001). As shown previously by analyzing real-world query logs, 1.5% of queries are explicitly provided with temporal criteria [19], i.e., containing temporal expressions, while about 7% of web queries have temporal intent implicitly provided [18].…”

Section: Introductionmentioning

confidence: 68%

Learning to rank search results for time-sensitive queries

Kanhabua

Nørvåg

2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

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Retrieval effectiveness of temporal queries can be improved by taking into account the time dimension. Existing temporal ranking models follow one of two main approaches: 1) a mixture model linearly combining textual similarity and temporal similarity, and 2) a probabilistic model generating a query from the textual and temporal part of document independently. In this paper, we propose a novel time-aware ranking model based on learning-to-rank techniques. We employ two classes of features for learning a ranking model, entity-based and temporal features, which are derived from annotation data. Entity-based features are aimed at capturing the semantic similarity between a query and a document, whereas temporal features measure the temporal similarity. Through extensive experiments we show that our ranking model significantly improves the retrieval effectiveness over existing time-aware ranking models.

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“…Several studies of real-world user query logs have shown that temporal queries comprises a significant fraction of web search queries. For example, Zhang et al (2010) showed that 13.8% of queries contain explicit time (Nunes et al (2008) reported 1.5%) and 17.1% of queries have a temporal intent implicitly provided (7% reported by Metzler et al (2009)). …”

Section: Changes In User Behaviormentioning

confidence: 99%