Books by Johannes Leveling
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Papers by Johannes Leveling
Lecture Notes in Computer Science, 2011
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Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, 2014
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Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, 2010
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Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, 2011
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Proceedings of the First Workshop on Personalised Multilingual Hypertext Retrieval, 2011
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Lecture Notes in Computer Science, 2011
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Proceedings of KONVENS 2006, 2006
In the so-called information society with its strong tendency towards individualization, it becom... more In the so-called information society with its strong tendency towards individualization, it becomes more and more important to have all sorts of textual information available in a simple and easy to understand language. We present an approach that allows to automatically rate the readability of German texts and also provides suggestions how to make a given text more readable. Our system, called DeLite, employs a powerful NLP component that supports the syntactic and semantic analysis of German texts.
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The classification of blind relevance feedback (BRF) terms described in this paper aims at increa... more The classification of blind relevance feedback (BRF) terms described in this paper aims at increasing precision or recall by determining which terms decrease, increase or do not change the corresponding information retrieval (IR) performance metric. Classification and IR experiments are performed on the German and English GIRT data, using the BM25 retrieval model. Several basic memory-based classifiers are trained on different feature sets, grouping together features from different query expansion (QE) approaches. Combined classifiers employ the results of the basic classifiers and correctness predictions as features. The best combined classifiers for German (English) yield 22.9% (26.4%) and 5.8% (1.9%) improvement for term classification wrt. precision and recall compared to the best basic classifiers. IR experiments based on this term classification have also been performed. Filtering out different types of BRF terms shows that selecting feedback terms predicted to increase precis...
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ACM Transactions on Asian Language Information Processing, 2010
The Forum for Information Retrieval Evaluation (FIRE) provides document collections, topics, and ... more The Forum for Information Retrieval Evaluation (FIRE) provides document collections, topics, and relevance assessments for information retrieval (IR) experiments on Indian languages. Several research questions are explored in this article: 1) How to create create a simple, language-independent corpus-based stemmer, 2) How to identify sub-words and which types of sub-words are suitable as indexing units, and 3) How to apply blind relevance feedback on sub-words and how feedback term selection is affected by the type of the indexing unit. More than 140 IR experiments are conducted using the BM25 retrieval model on the topic titles and descriptions (TD) for the FIRE 2008 English, Bengali, Hindi, and Marathi document collections. The major findings are: The corpus-based stemming approach is effective as a knowledge-light term conflation step and useful in the case of few language-specific resources. For English, the corpus-based stemmer performs nearly as well as the Porter stemmer and ...
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Lecture Notes in Computer Science, 2011
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Books by Johannes Leveling
Papers by Johannes Leveling