Topic Difficulty Prediction in Entity Ranking
Identifieur interne : 000217 ( Main/Exploration ); précédent : 000216; suivant : 000218Topic Difficulty Prediction in Entity Ranking
Auteurs : Anne-Marie Vercoustre [France] ; Jovan Pehcevski [Macédoine (pays), République de Macédoine (pays)] ; Vladimir Naumovski [Macédoine (pays), République de Macédoine (pays)]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2009.
Abstract
Abstract: Entity ranking has recently emerged as a research field that aims at retrieving entities as answers to a query. Unlike entity extraction where the goal is to tag the names of the entities in documents, entity ranking is primarily focused on returning a ranked list of relevant entity names for the query. Many approaches to entity ranking have been proposed, and most of them were evaluated on the INEX Wikipedia test collection. In this paper, we show that the knowledge of predicted classes of topic difficulty can be used to further improve the entity ranking performance. To predict the topic difficulty, we generate a classifier that uses features extracted from an INEX topic definition to classify the topic into an experimentally pre-determined class. This knowledge is then utilised to dynamically set the optimal values for the retrieval parameters of our entity ranking system. Our experiments suggest that topic difficulty prediction is a promising approach that could be exploited to improve the effectiveness of entity ranking.
Url:
DOI: 10.1007/978-3-642-03761-0_29
Affiliations:
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Unlike entity extraction where the goal is to tag the names of the entities in documents, entity ranking is primarily focused on returning a ranked list of relevant entity names for the query. Many approaches to entity ranking have been proposed, and most of them were evaluated on the INEX Wikipedia test collection. In this paper, we show that the knowledge of predicted classes of topic difficulty can be used to further improve the entity ranking performance. To predict the topic difficulty, we generate a classifier that uses features extracted from an INEX topic definition to classify the topic into an experimentally pre-determined class. This knowledge is then utilised to dynamically set the optimal values for the retrieval parameters of our entity ranking system. Our experiments suggest that topic difficulty prediction is a promising approach that could be exploited to improve the effectiveness of entity ranking.</div></front></TEI><affiliations><list><country><li>France</li><li>Macédoine (pays)</li><li>République de Macédoine (pays)</li></country></list><tree><country name="France"><noRegion><name sortKey="Vercoustre, Anne Marie" sort="Vercoustre, Anne Marie" uniqKey="Vercoustre A" first="Anne-Marie" last="Vercoustre">Anne-Marie Vercoustre</name></noRegion><name sortKey="Vercoustre, Anne Marie" sort="Vercoustre, Anne Marie" uniqKey="Vercoustre A" first="Anne-Marie" last="Vercoustre">Anne-Marie Vercoustre</name></country><country name="Macédoine (pays)"><noRegion><name sortKey="Pehcevski, Jovan" sort="Pehcevski, Jovan" uniqKey="Pehcevski J" first="Jovan" last="Pehcevski">Jovan Pehcevski</name></noRegion><name sortKey="Naumovski, Vladimir" sort="Naumovski, Vladimir" uniqKey="Naumovski V" first="Vladimir" last="Naumovski">Vladimir Naumovski</name></country><country name="République de Macédoine (pays)"><noRegion><name sortKey="Pehcevski, Jovan" sort="Pehcevski, Jovan" uniqKey="Pehcevski J" first="Jovan" last="Pehcevski">Jovan Pehcevski</name></noRegion><name sortKey="Naumovski, Vladimir" sort="Naumovski, Vladimir" uniqKey="Naumovski V" first="Vladimir" last="Naumovski">Vladimir Naumovski</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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