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Erschienen in:
You Can’t Learn What’s Not There: Self Supervised Learning and the Poverty of the Stimulus Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

Multilevel Entity-Informed Business Relation Extraction

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

Erschienen in: Natural Language Processing and Information Systems

Verlag: 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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Vorheriges Kapitel Virus Causes Flu: Identifying Causality in the Biomedical Domain Using an Ensemble Approach with Target-Specific Semantic Embeddings
Nächstes Kapitel The Importance of Character-Level Information in an Event Detection Model
Fußnoten
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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Metadaten
Titel
Multilevel Entity-Informed Business Relation Extraction
verfasst von
Hadjer Khaldi
Farah Benamara
Amine Abdaoui
Nathalie Aussenac-Gilles
EunBee Kang
Copyright-Jahr
2021
Buch
Natural Language Processing and Information Systems

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

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

Copyright-Jahr: 2021

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

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