A Language Modeling Approach for Temporal Information Needs
Identifieur interne : 001240 ( Istex/Curation ); précédent : 001239; suivant : 001241A Language Modeling Approach for Temporal Information Needs
Auteurs : Klaus Berberich [Allemagne] ; Srikanta Bedathur [Allemagne] ; Omar Alonso [Allemagne] ; Gerhard Weikum [Allemagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2010.
English descriptors
- Teeft :
- Account publication times, Berberich, Best performance, Best retrieval performance, Business news archives, Concrete example, Document collection, Equal likelihood, Exact time interval, Exclusive mode, Experimental evaluation, Fifa world, Generative model, Inclusive mode, Inclusive mode method, Indicator function, Information retrieval, Inherent uncertainty, July, Keith harring, Language model, Language model retrieval framework, Language modeling approach, Language models, Modeling, Novel approach, Publication times, Query, Relevance assessments, Relevant documents, Retrieval, Retrieval model, Retrieval models, Second half, Sigir forum, Temporal, Temporal annotation, Temporal dimension, Temporal expression, Temporal expressions, Temporal granularity, Temporal information, Temporal language models, Temporal part, Temporal queries, Textual, Textual part, Textual terms, Third option, Time interval, Time intervals, Timeline view, Unigram language model, Unix epoch, User studies, York times.
Abstract
Abstract: This work addresses information needs that have a temporal dimension conveyed by a temporal expression in the user’s query. Temporal expressions such as “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.
Url:
DOI: 10.1007/978-3-642-12275-0_5
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<front><div type="abstract" xml:lang="en">Abstract: This work addresses information needs that have a temporal dimension conveyed by a temporal expression in the user’s query. Temporal expressions such as “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.</div>
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