Word association norms, mutual information, and lexicography
Computational Linguistics
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EMCL '01 Proceedings of the 12th European Conference on Machine Learning
EPIA '99 Proceedings of the 9th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
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ACM Transactions on Information Systems (TOIS)
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IEEE Transactions on Knowledge and Data Engineering
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Universal Mobile Information Retrieval
UAHCI '09 Proceedings of the 5th International on ConferenceUniversal Access in Human-Computer Interaction. Part II: Intelligent and Ubiquitous Interaction Environments
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AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
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CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
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ECDL'10 Proceedings of the 14th European conference on Research and advanced technology for digital libraries
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SPIRE'11 Proceedings of the 18th international conference on String processing and information retrieval
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
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Generically, search engines fail to understand the user's temporal intents when expressed as implicit temporal queries. This causes the retrieval of less relevant information and prevents users from being aware of the possible temporal dimension of the query results. In this paper, we aim to develop a language-independent model that tackles the temporal dimensions of a query and identifies its most relevant time periods. For this purpose, we propose a temporal similarity measure capable of associating a relevant date(s) to a given query and filtering out irrelevant ones. Our approach is based on the exploitation of temporal information from web content, particularly within the set of k-top retrieved web snippets returned in response to a query. We particularly focus on extracting years, which are a kind of temporal information that often appears in this type of collection. We evaluate our methodology using a set of real-world text temporal queries, which are clear concepts (i.e. queries which are non-ambiguous in concept and temporal in their purpose). Experiments show that when compared to baseline methods, determining the most relevant dates relating to any given implicit temporal query can be improved with a new temporal similarity measure.