Abstract
Extracting attribute-value information from unstructured product descriptions continue to be of a vital importance in e-commerce applications. One of the most important product attributes is the brand which highly influences costumers’ purchasing behaviour. Thus, it is crucial to accurately extract brand information dealing with the main challenge of discovering new brand names. Under the open world assumption, several approaches have adopted deep learning models to extract attribute-values using sequence tagging paradigm. However, they did not employ finer grained data representations such as character level embeddings which improve generalizability. In this paper, we introduce OpenBrand, a novel approach for discovering brand names. OpenBrand is a BiLSTM-CRF-Attention model with embeddings at different granularities. Such embeddings are learned using CNN and LSTM architectures to provide more accurate representations. We further propose a new dataset for brand value extraction, with a very challenging task on zero-shot extraction. We have tested our approach, through extensive experiments, and shown that it outperforms state-of-the-art models in brand name discovery.- Anthology ID:
- 2022.ecnlp-1.19
- Volume:
- Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- ECNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 161–170
- Language:
- URL:
- https://aclanthology.org/2022.ecnlp-1.19
- DOI:
- 10.18653/v1/2022.ecnlp-1.19
- Cite (ACL):
- Kassem Sabeh, Mouna Kacimi, and Johann Gamper. 2022. OpenBrand: Open Brand Value Extraction from Product Descriptions. In Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5), pages 161–170, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- OpenBrand: Open Brand Value Extraction from Product Descriptions (Sabeh et al., ECNLP 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.ecnlp-1.19.pdf
- Code
- kassemsabeh/open-brand