Abstract
A particular problem of searching news archives with named entities is that they are very dynamic in appearance compared to other vocabulary terms, and synonym relationships between terms change with time. In previous work, we proposed an approach to extracting time-based synonyms of named entities from the whole history of Wikipedia. In this paper, we present QUEST (Query Expansion using Synonyms over Time), a system that exploits time-based synonyms in searching news archives. The system takes as input a named entity query, and automatically determines time-based synonyms for a given query wrt. time criteria. Query expansion using the determined synonyms can be employed in order to improve the retrieval effectiveness.
This work has been supported by the LongRec project, partially funded by the Norwegian Research Council.
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Kanhabua, N., Nørvåg, K. (2010). QUEST: Query Expansion Using Synonyms over Time. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2010. Lecture Notes in Computer Science(), vol 6323. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15939-8_41
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DOI: https://doi.org/10.1007/978-3-642-15939-8_41
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