Edgar Meij

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Edgar Meij is an author.


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Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Adding semantics to microblog posts Algorithms
WSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining English 2012 Microblogs have become an important source of information for the purpose of marketing, intelligence, and reputation management. Streams of microblogs are of great value because of their direct and real-time nature. Determining what an individual microblog post is about, however, can be non-trivial because of creative language usage, the highly contextualized and informal nature of microblog posts, and the limited length of this form of communication. We propose a solution to the problem of determining what a microblog post is about through semantic linking: we add semantics to posts by automatically identifying concepts that are semantically related to it and generating links to the corresponding Wikipedia articles. The identified concepts can subsequently be used for, e.g., social media mining, thereby reducing the need for manual inspection and selection. Using a purpose-built test collection of tweets, we show that recently proposed approaches for semantic linking do not perform well, mainly due to the idiosyncratic nature of microblog posts. We propose a novel method based on machine learning with a set of innovative features and show that it is able to achieve significant improvements over all other methods, especially in terms of precision. Copyright 2012 ACM. 0 0
Supervised query modeling using Wikipedia English 2010 We use Wikipedia articles to semantically inform the generation of query models. To this end, we apply supervised machine learning to automatically link queries to Wikipedia articles and sample terms from the linked articles to re-estimate the query model. On a recent large web corpus, we observe substantial gains in terms of both traditional metrics and diversity measures. 0 0
The university of amsterdam at TREC 2010: Session, entity, and relevance feedback NIST Special Publication English 2010 We describe the participation of the University of Amsterdam's ILPS group in the session, entity, and relevance feedback track at TREC 2010. In the Session Track we explore the use of blind relevance feedback to bias a follow-up query towards or against the topics covered in documents returned to the user in response to the original query. In the Entity Track REF task we experiment with a window size parameter to limit the amount of context considered by the entity co-occurrence models and explore the use of Freebase for type filtering, entity normalization and homepage finding. In the ELC task we use an approach that uses the number of links shared between candidate and example entities to rank candidates. In the Relevance Feedback Track we experiment with a novel model that uses Wikipedia to expand the query language model. 0 0
Investigating the demand side of semantic search through query log analysis CEUR Workshop Proceedings English 2009 In this paper, we propose a method to create aggregated representations of the information needs of Web users when searching for particular types of objects. We suggest this method as a way to investigate the gap between what Web search users are expecting to find and the kind of information that is provided by Semantic Web datasets formatted according to a particular ontology. We evaluate our method qualitatively by measuring its power as a query completion mechanism. Last, we perform a qualitative evaluation comparing the information Web users search for with the information available in Dbpedia, the structured data representation of Wikipedia. 0 0