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キーワード:
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要旨:
This work addresses information needs that have a temporal
dimension conveyed by a temporal expression in the
user's query. Temporal expressions such as \textsf{``in the 1990s''}
are
frequent, easily extractable, but not leveraged by existing
retrieval models. One challenge when dealing with them is their
inherent uncertainty. It is often unclear which exact time interval
a temporal expression refers to.
We integrate temporal expressions into a language modeling approach,
thus making them first-class citizens of the retrieval model and
considering their inherent uncertainty. Experiments on the New York
Times Annotated Corpus using Amazon Mechanical Turk to collect
queries and obtain relevance assessments demonstrate that
our approach yields substantial improvements in retrieval
effectiveness.
