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Résumé:
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The development of systems that extract a frame representation of text can lead to deeper
semantics being used in natural language processing. We present the development of our
system for extracting frames from text. Our system is trained on the FrameNet data and tested
on the SemEval 2007: Task 19 Frame Extraction Task data. We use machine learning for
labeling frames and frame elements, resulting in system with a good performance. We
provide a detailed analysis of our methods, challenges, and results. We also provide enough
details and analysis to allow other researchers to develop similar systems. |
