Temporal Information Access (Temporalia-2)
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1 Temporal Information Access (Temporalia-2) NTCIR-12 Core Task Hideo Joho (University of Tsukuba) Adam Jatowt (Kyoto University) Roi Blanco (Yahoo! Research) Haitao Yu (University of Tsukuba) Shuhei Yamamoto (University of Tsukuba)
2 Motivation: Temporal IR 1.5% of queries are explicit temporal queries Germany 1920s, Olympics 1956 Many queries are implicitly temporal, e.g.: einstein life, fashion trend, tokyo weather, olympics Temporal queries require special treatment By matching query temporality with document temporality Arikan, I., Bedathur, S.J. and Berberich, K. Time Will Tell: Leveraging Temporal Expressions in IR. WSDM2009 Berberich et al. Language Modeling Approach for Temporal Information Needs. ECIR 2010 Kanhabua, N. and Norvag,K., Determining Time of Queries for Reranking Search Results. ECDL2010 Nunes, S., Ribeiro, C., and David, G. Use of Temporal Expressions in Web Search. ECIR2008 Campos, R., Dias, G. Jorge, A.M., and Jatowt, A. Survey of Temporal Information Retrieval and Related Applications. ACM Comput. Surv. 47(2): 15 (2014)
3 Motivation: Importance of Time in IR Example results that do not match query temporal intent: tokyo weather doc with yesterday s results (?) economic forecast japan doc with past forecasts (?) previous tokyo olympics doc about Olympics 2020 (?)
4 (PAST) Temporalia-1: English Lang. 1. Temporal Query Intent Classification (TQIC) Classify query to temporal classes 2. Temporal Information Retrieval (TIR) Rank documents for temporal queries Team ID Institution Country TQIC TIR HITSZ Graduate School of Harbin Institute of Technology at Shenzhen P.R.C. HULTECH University of Caen France TUTA1 University of Tokushima Japan Andd7 Dhirubhai Ambani Institute of Information and Communication Technology India MPII Max Planck Institute for Informatics Germany UniMAN University of Manchester UK BRKLY U.C. Berkeley USA OSKAT Osaka Kyoiku University Japan ORG Temporalia Organizers Japan, Spain
5 (Current) Temporalia-2: English & Chinese Lang. 1. Temporal Intent Disambiguation (TID) For a given query, determine probability distribution over temporal intent classes Input: query, query timestamp Output: temporal intent class distribution 2. Temporally Diversified Retrieval (TIR) For a given topic, retrieve documents relevant to each of temporal intent classes Input: set of topic descriptions, timestamp and fixed temporal intent class distribution Output: diversified ranking of documents
6 Temporal Classes of Queries/Topics On recent things/events On past things/events Recency Past Query/Topic On future things/events Future Atemporal No temporal intent or navigational
7 English Document Collection LivingKnowledge Corpus [ Crawl time: April 2011 and March 2013 Source: news and blog feeds Size: 20GB uncompressed (>5GB zipped) 3.8 million documents from 1500 different blogs and news sources Text only Provided annotations: Temporal Expressions, Named Entities and Sentence Boundaries Participants can choose to use annotations or not Participants can use external collections such as Wikipedia
8 Chinese Document Collection Dry Run: SogouCA, a collection of news web pages Time span: June and July in 2012 Source: several popular news websites in China Size: the total uncompressed size is 711MB Formal Run: Distilled subset of the web page corpus SogouT 2012 Crawl time: mid 2012 Size: subset of million Web pages from 5.3 million Chinese Web sites Provided annotations: Similar as in the English collection
9 Draft Schedule
10 Thank you! Website: Facebook: Twitter:
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