Temporal Information Access(Temporalia-2)
NTCIR-12 Core Task
http://ntcirtemporalia.github.io
Hideo Joho (University of Tsukuba)
Adam Jatowt (Kyoto University)
Roi Blanco (Yahoo! Research)
Haitao Yu (University of Tsukuba)
Shuhei Yamamoto (University of Tsukuba)

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)

Temporalia

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 (?)

Temporalia(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

Temporalia

Team IDInstitutionCountryTQIC

TIR

HITSZ

Graduate School of Harbin Institute ofTechnology at Shenzhen

P.R.C.

HULTECH

University of Caen

FranceTUTA1

University of Tokushima

Japan





Andd7

Dhirubhai Ambani Institute of Information
and Communication TechnologyIndiaMPIIMax Planck Institute for Informatics

GermanyUniMAN

University of Manchester

UK

BRKLY

U.C. Berkeley

USA



OSKAT

Osaka Kyoiku UniversityJapan

ORG

Temporalia Organizers

Japan, Spain

(Current) Temporalia-2: English &Chinese Lang.
1. Temporal Intent Disambiguation (TID)• For a given query, determine probability distribution overtemporal 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
TemporaliaTemporal Classes of Queries/Topics
On recentthings/events

On pastthings/events

Recency Past Query/TopicFuture On futurethings/events

Temporalia

Atemporal
No temporal
intent or
navigational

English Document Collection• LivingKnowledge Corpus [http://livingknowledge.europarchive.org/]• 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 andSentence Boundaries• Participants can choose to use annotations or not• Participants can use external collections such as Wikipedia

Temporalia

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 135.4 million Web pages from 5.3 million Chinese Web sites• Provided annotations:• Similar as in the English collection

TemporaliaDraft Schedule

TemporaliaThank you!
Website: http://ntcirtemporalia.github.io
Facebook: https://www.facebook.com/ntcirtemporalia
Twitter: https://twitter.com/ntcirtemporalia
Email: tc4fia@googlegroups.com

Temporalia