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Conference papers
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.
Cited literature [22 references]
https://hal.archives-ouvertes.fr/hal-01913664
Contributor : Open Archive Toulouse Archive Ouverte (oatao) <>
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
Contributor : Open Archive Toulouse Archive Ouverte (oatao) <>
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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