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Unsupervised relation extraction from scientific texts using self-organizing maps

Abstract : Scientific texts represent a rich source of unstructured knowledge. Extracting this knowledge in a supervised manner can become highly expensive in time and human resources. Moreover supervised models are domain- and language-dependent which make them hard to maintain and extend. Hence unsupervised methods have received a lot of attention from researchers in the fields of information extraction and data mining. In this paper, we present our experiments with self-organizing maps (SOMs) for the task of open relation extraction. We combine contextual features of different level (lemmas and parts-of-speech) to help the algorithm to automatically discover lexical and morphological patterns in the corpus. The evaluation results show that our model yields a better performance than the widely used K-means clustering algorithm with the same feature set.
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Submitted on : Tuesday, November 6, 2018 - 2:36:57 PM
Last modification on : Tuesday, September 1, 2020 - 7:08:03 PM
Long-term archiving on: : Thursday, February 7, 2019 - 2:45:17 PM


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  • HAL Id : hal-01913664, version 1
  • OATAO : 19106


Elena Manishina, Mouna Kamel, Cassia Trojahn, Nathalie Aussenac-Gilles. Unsupervised relation extraction from scientific texts using self-organizing maps. 1er Atelier sur l' Extraction et la Modélisation de Connaissances à partir de textes scientifiques, associé à PFIA 2017 (EMC-Sci 2017), Jul 2017, Caen, France. pp.25-32. ⟨hal-01913664⟩



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