Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations

Arkadiusz Janz, Łukasz Kopociński, Maciej Piasecki, Agnieszka Pluwak


Abstract
Relation Extraction is a fundamental NLP task. In this paper we investigate the impact of underlying text representation on the performance of neural classification models in the task of Brand-Product relation extraction. We also present the methodology of preparing annotated textual corpora for this task and we provide valuable insight into the properties of Brand-Product relations existing in textual corpora. The problem is approached from a practical angle of applications Relation Extraction in facilitating commercial Internet monitoring.
Anthology ID:
2020.lrec-1.233
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1895–1901
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.233
DOI:
Bibkey:
Cite (ACL):
Arkadiusz Janz, Łukasz Kopociński, Maciej Piasecki, and Agnieszka Pluwak. 2020. Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1895–1901, Marseille, France. European Language Resources Association.
Cite (Informal):
Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations (Janz et al., LREC 2020)
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PDF:
https://preview.aclanthology.org/author-url/2020.lrec-1.233.pdf