Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations
2020-05-01LREC 2020Unverified0· sign in to hype
Arkadiusz Janz, {\L}ukasz Kopoci{\'n}ski, Maciej Piasecki, Agnieszka Pluwak
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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.