Missing data handling is an important preparation step for most data discrimination or mining tasks. Inappropriate treatment of missing data may cause large errors or false result...
In a data warehousing process, the data preparation phase is crucial. Mastering this phase allows substantial gains in terms of time and performance when performing a multidimensio...
Fundamental to data cleaning is the need to account for multiple data representations. We propose a formal framework that can be used to reason about and manipulate data represent...
A system to automatically transcribe lectures and presentations has been developed in the context of the FP6 Integrated Project CHIL. In addition to the seminar data recorded by th...
Lori Lamel, Eric Bilinski, Jean-Luc Gauvain, Gille...
We describe two corpora of question and answer pairs collected for complex, open-domain Question Answering (QA) to enable answer classification and re-ranking experiments. We deli...