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Unsupervised relation extraction from scientific texts using self-organizing maps

Manishina, Elena and Kamel, Mouna and Trojahn, Cassia and Aussenac-Gilles, Nathalie Unsupervised relation extraction from scientific texts using self-organizing maps. (2017) In: 1er Atelier sur l' Extraction et la Modélisation de Connaissances à partir de textes scientifiques, associé à PFIA 2017 (EMC-Sci 2017), 3 July 2017 - 3 July 2017 (Caen, France).

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Official URL: https://pdfs.semanticscholar.org/be35/ff462fadc7803086661e64dacf100b396a83.pdf

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.

Item Type:Conference or Workshop Item (Paper)
HAL Id:hal-01913664
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
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Deposited By: IRIT IRIT
Deposited On:05 Oct 2018 13:30

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