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
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 1895–1901
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.233
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.lrec-1.233.pdf