Foundations of statistical natural language processing
Foundations of statistical natural language processing
Analysis of lexical signatures for improving information persistence on the World Wide Web
ACM Transactions on Information Systems (TOIS)
From temporal expressions to temporal information: semantic tagging of news messages
TASIP '01 Proceedings of the workshop on Temporal and spatial information processing - Volume 13
Introduction to Information Retrieval
Introduction to Information Retrieval
Improving search relevance for implicitly temporal queries
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Temporal processing with the TARSQI toolkit
COLING '08 22nd International Conference on on Computational Linguistics: Demonstration Papers
SemEval-2007 task 15: TempEval temporal relation identification
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Topic detection and tracking with spatio-temporal evidence
ECIR'03 Proceedings of the 25th European conference on IR research
Use of temporal expressions in web search
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
SemEval-2010 task 13: TempEval-2
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
TIPSem (English and Spanish): Evaluating CRFs and semantic roles in TempEval-2
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
TimeTrails: a system for exploring spatio-temporal information in documents
Proceedings of the VLDB Endowment
Temporal analysis of document collections: framework and applications
SPIRE'10 Proceedings of the 17th international conference on String processing and information retrieval
An event-centric model for multilingual document similarity
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Detecting seasonal queries by time-series analysis
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Enhancing document snippets using temporal information
SPIRE'11 Proceedings of the 18th international conference on String processing and information retrieval
Studying how the past is remembered: towards computational history through large scale text mining
Proceedings of the 20th ACM international conference on Information and knowledge management
A language modeling approach for temporal information needs
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Proceedings of the 21st ACM international conference on Information and knowledge management
SPIRE'12 Proceedings of the 19th international conference on String Processing and Information Retrieval
Disambiguating Implicit Temporal Queries by Clustering Top Relevant Dates in Web Snippets
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Estimating document focus time
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Temporal information is very common in textual documents, and thus, identifying, normalizing, and organizing temporal expressions is an important task in IR. Although there are some tools for temporal tagging, there is a lack in research focusing on the relevance of temporal expressions. Besides counting their frequency and verifying whether they satisfy a temporal search query, temporal expressions are often considered in isolation only. There are no methods to calculate the relevance of temporal expressions, neither in general nor with respect to a query. In this paper, we present an approach to identify top relevant temporal expressions in documents using expression-, document-, corpus-, and query-based features. We present two relevance functions: one to calculate relevance scores for temporal expressions in general, and one with respect to a search query, which consists of a textual part, a temporal part, or both. Using two evaluation scenarios, we demonstrate the effectiveness of our approach.