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Conference papers

Active Learning for Interactive Relation Extraction in a French Newspaper's Articles

Abstract : Relation extraction is a subtask of natural language processing that has seen many improvements in recent years, with the advent of complex pre-trained architectures. Many of these state-of-the-art approaches are tested against benchmarks with labelled sentences containing tagged entities, and require important pretraining and fine-tuning on task-specific data. However, in a real use-case scenario such as in a newspaper company mostly dedicated to local information, relations are of varied, highly specific type, with virtually no annotated data for such relations, and many entities co-occur in a sentence without being related. We question the use of supervised state-of-the-art models in such a context, where resources such as time, computing power and human annotators are limited. To adapt to these constraints, we experiment with an active-learning based relation extraction pipeline, consisting of a binary LSTM-based lightweight model for detecting the relations that do exist, and a state-of-the-art model for relation classification. We compare several choices for classification models in this scenario, from basic word embedding averaging, to graph neural networks and Bert-based ones, as well as several active learning acquisition strategies, in order to find the most costefficient yet accurate approach in our French largest daily newspaper company's use case.
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Contributor : Pascale Sébillot Connect in order to contact the contributor
Submitted on : Saturday, October 9, 2021 - 9:54:09 AM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM
Long-term archiving on: : Monday, January 10, 2022 - 6:07:56 PM


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  • HAL Id : hal-03371917, version 1


Cyrielle Mallart, Michel Le Nouy, Guillaume Gravier, Pascale Sébillot. Active Learning for Interactive Relation Extraction in a French Newspaper's Articles. RANLP 2021 - Recent Advances in Natural Language Processing, Sep 2021, Online, Bulgaria. pp.886-894. ⟨hal-03371917⟩



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