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Learning to Detect Event-Related Queries for Web Search

Published: 18 May 2015 Publication History

Abstract

In many cases, a user turns to search engines to find information about real-world situations, namely, political elections, sport competitions, or natural disasters. Such temporal querying behavior can be observed through a significant number of event-related queries generated in web search. In this paper, we study the task of detecting event-related queries, which is the first step for understanding temporal query intent and enabling different temporal search applications, e.g., time-aware query auto-completion, temporal ranking, and result diversification. We propose a two-step approach to detecting events from query logs. We first identify a set of event candidates by considering both implicit and explicit temporal information needs. The next step further classifies the candidates into two main categories, namely, event or non-event. In more detail, we leverage different machine learning techniques for query classification, which are trained using the feature set composed of time series features from signal processing, along with features derived from click-through information, and standard statistical features. In order to evaluate our proposed approach, we conduct an experiment using two real-world query logs with manually annotated relevance assessments for 837 events. To this end, we provide a large set of event-related queries made available for fostering research on this challenging task.

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  • (2020)Event-Related Query Classification with Deep Neural NetworksCompanion Proceedings of the Web Conference 202010.1145/3366424.3382183(324-330)Online publication date: 20-Apr-2020
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  1. Learning to Detect Event-Related Queries for Web Search

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    cover image ACM Other conferences
    WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web
    May 2015
    1602 pages
    ISBN:9781450334730
    DOI:10.1145/2740908

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    • IW3C2: International World Wide Web Conference Committee

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 18 May 2015

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    Author Tags

    1. events
    2. query intent
    3. temporal query classification

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    • Research-article

    Funding Sources

    • The European Commission for the ERC Advanced Grant ALEXANDRIA
    • The European Commission for the FP7 project ForgetIT

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    WWW '15
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    • IW3C2

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    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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    • (2025)Single-Turn Natural Language UnderstandingNatural Language Understanding in Conversational AI with Deep Learning10.1007/978-3-031-74364-1_3(49-85)Online publication date: 12-Jan-2025
    • (2021)Event-Driven Query ExpansionProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441833(391-399)Online publication date: 8-Mar-2021
    • (2020)Event-Related Query Classification with Deep Neural NetworksCompanion Proceedings of the Web Conference 202010.1145/3366424.3382183(324-330)Online publication date: 20-Apr-2020
    • (2020)Deep Learning for Adverse Event Detection from Web SearchIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.3017786(1-1)Online publication date: 2020
    • (2020)Query Intent UnderstandingQuery Understanding for Search Engines10.1007/978-3-030-58334-7_4(69-101)Online publication date: 2-Dec-2020
    • (2019)$$\hbox {NE}^2$$NE2Knowledge and Information Systems10.1007/s10115-018-1208-859:2(311-335)Online publication date: 1-May-2019
    • (2018)JIMProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271681(637-646)Online publication date: 17-Oct-2018
    • (2018)Automatic prediction of news intent for search queriesThe Electronic Library10.1108/EL-06-2017-013436:5(938-958)Online publication date: Oct-2018
    • (2018)Multiple Models for Recommending Temporal Aspects of EntitiesThe Semantic Web10.1007/978-3-319-93417-4_30(462-480)Online publication date: 3-Jun-2018
    • (2017)Learning Temporal Ambiguity in Web Search QueriesProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3133129(2191-2194)Online publication date: 6-Nov-2017
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