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You Can’t Learn What’s Not There: Self Supervised Learning and the Poverty of the Stimulus Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Multilevel Entity-Informed Business Relation Extraction

Authors : Hadjer Khaldi, Farah Benamara, Amine Abdaoui, Nathalie Aussenac-Gilles, EunBee Kang

Published in: Natural Language Processing and Information Systems

Publisher: Springer International Publishing

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Abstract

This paper describes a business relation extraction system that combines contextualized language models with multiple levels of entity knowledge. Our contributions are three-folds: (1) a novel characterization of business relations, (2) the first large English dataset of more than 10k relation instances manually annotated according to this characterization, and (3) multiple neural architectures based on BERT, newly augmented with three complementary levels of knowledge about entities: generalization over entity type, pre-trained entity embeddings learned from two external knowledge graphs, and an entity-knowledge-aware attention mechanism. Our results show an improvement over many strong knowledge-agnostic and knowledge-enhanced state of the art models for relation extraction.

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Footnotes
2
We consider textual contents from various sources and formats excluding those retrieved from social media, e-commerce, and code versioning websites.
 
3
The set of keywords have been chosen by business intelligence experts.
 
7
All the hyperparameters were tuned on a validation set (10% of the train set).
 
8
Among existing entity-informed models (cf. Sect. 2), at the time of performing these experiments, and as far as we know, only KnowBert and ERNIE were actually available to the research community. In this paper, we compare with Knowbert as it achieved the best results on the TACRED dataset (71.50% on F1-score) when compared to ERNIE (67.97%) [25].
 
9
We also experimented with Entity-Attention-BiLSTM following [10] but the results were not conclusive.
 
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Metadata
Title
Multilevel Entity-Informed Business Relation Extraction
Authors
Hadjer Khaldi
Farah Benamara
Amine Abdaoui
Nathalie Aussenac-Gilles
EunBee Kang
Copyright Year
2021
Book
Natural Language Processing and Information Systems

Print ISBN: 978-3-030-80598-2

Electronic ISBN: 978-3-030-80599-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-80599-9_10

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