Disambiguating Implicit Temporal Queries by Clustering Top Relevant Dates in Web Snippets

With the growing popularity of research in Temporal Information Retrieval (T-IR), a large amount of temporal data is ready to be exploited. The ability to exploit this information can be potentially useful for several tasks. For example, when querying "Football World Cup Germany", it would...

Full description

Saved in:
Bibliographic Details
Published in:2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Vol. 1; pp. 1 - 8
Main Authors: Campos, Ricardo, Jorge, Alipio Mario, Dias, Gael, Nunes, Celia
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2012
Subjects:
Classification algorithms
Clustering algorithms
Correlation
Dating Implicit Temporal Queries
Engines
Feature extraction
Mathematical models
Search engines
Temporal Clustering
Temporal Information Retrieval
Temporal Query Understanding
Vectors
Visualization
Web search
ISBN:9781467360579, 1467360570
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:With the growing popularity of research in Temporal Information Retrieval (T-IR), a large amount of temporal data is ready to be exploited. The ability to exploit this information can be potentially useful for several tasks. For example, when querying "Football World Cup Germany", it would be interesting to have two separate clusters {1974,2006} corresponding to each of the two temporal instances. However, clustering of search results by time is a non-trivial task that involves determining the most relevant dates associated to a query. In this paper, we propose a first approach to flat temporal clustering of search results. We rely on a second order co-occurrence similarity measure approach which first identifies top relevant dates. Documents are grouped at the year level, forming the temporal instances of the query. Experimental tests were performed using real-world text queries. We used several measures for evaluating the performance of the system and compared our approach with Carrot Web-snippet clustering engine. Both experiments were complemented with a user survey.
ISBN:9781467360579
1467360570
DOI:10.1109/WI-IAT.2012.158