CLEF 2010 Call for lab proposals
October 21, 2009 11:41 | Permalink
The CLEF 2010 conference is the continuation of the
popular CLEF campaigns and workshops that have run for
the past ten years (2000-2009). It will cover a broad
range of issues from fields of multilingual and
multimodal information access evaluation. One of the
two main parts of CLEF 2010 will be a series of "labs".
Two different forms of labs will be offered: labs can
either be run "campaign-style" during the twelve month
period preceding the conference, or adopt a more
"workshop"-style format that can explore issues of
information access evaluation and related fields. The
labs will culminate in sessions of a half-day, one full
day or two days at the CLEF 2010 conference. Read more ...
Listening to ''Can Vei La Lauzeta Mover'', by Studio Der Frühen Musik (Play Count: 13)
Listening to ''Can Vei La Lauzeta Mover'', by Studio Der Frühen Musik (Play Count: 13)
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IPM paper online
Conceptual Languages for Domain-Specific
Retrieval by Edgar Meij, Dolf Trieschnigg, Maarten
de Rijke and Wessel Kraaij was accepted for publication
in Information Processing and Management a while
back; it is available
now. Over the years, various meta-languages have been
used to manually enrich documents with conceptual
knowledge of some kind. Examples include keyword
assignment to citations or, more recently, tags to
websites. In this paper we propose generative concept
models as an extension to query modeling within the
language modeling framework, which leverages these
conceptual annotations to improve retrieval. By means
of relevance feedback the original query is translated
into a conceptual representation, which is subsequently
used to update the query model.
Extensive experimental work on five test collections in two domains shows that our approach gives significant improvements in terms of recall, initial precision and mean average precision with respect to a baseline without relevance feedback. On one test collection, it is also able to outperform a text-based pseudo-relevance feedback approach based on relevance models. On the other test collections it performs similarly to relevance models. Overall, conceptual language models have the added advantage of offering query and browsing suggestions in the form of conceptual annotations. In addition, the internal structure of the meta-language can be exploited to add related terms.
Our contributions are threefold. First, an extensive study is conducted on how to effectively translate a textual query into a conceptual representation. Second, we propose a method for updating a textual query model using the concepts in conceptual representation. Finally, we provide an extensive analysis of when and how this conceptual feedback improves retrieval.
iTunes is not playing.
Extensive experimental work on five test collections in two domains shows that our approach gives significant improvements in terms of recall, initial precision and mean average precision with respect to a baseline without relevance feedback. On one test collection, it is also able to outperform a text-based pseudo-relevance feedback approach based on relevance models. On the other test collections it performs similarly to relevance models. Overall, conceptual language models have the added advantage of offering query and browsing suggestions in the form of conceptual annotations. In addition, the internal structure of the meta-language can be exploited to add related terms.
Our contributions are threefold. First, an extensive study is conducted on how to effectively translate a textual query into a conceptual representation. Second, we propose a method for updating a textual query model using the concepts in conceptual representation. Finally, we provide an extensive analysis of when and how this conceptual feedback improves retrieval.
iTunes is not playing.