A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Extracting meaningful entities from police narrative reports
dg.o '02 Proceedings of the 2002 annual national conference on Digital government research
dg.o '08 Proceedings of the 2008 international conference on Digital government research
Training Conditional Random Fields by Periodic Step Size Adaptation for Large-Scale Text Mining
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
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The progressive deployment of ICT technologies in the courtroom is leading to the development of integrated multimedia folders where the entire trial contents (documents, audio and video recordings) are available for online consultation via web-based platforms. The current amount of unstructured textual data available into the judicial domain, especially related to hearing transcriptions, highlights therefore the need to automatically extract structured data from the unstructured ones for improving the efficiency of consultation processes. In this paper we address the problem of extracting structured information from the transcriptions generated automatically using an ASR (Automatic Speech Recognition) system, by integrating Conditional Random Fields with available background information. The computational experiments show promising results in structuring ASR outputs, enabling a robust and efficient document consultation.