TY - GEN
T1 - Is It Out Yet? Automatic Future Product Releases Extraction from Web Data
AU - Fuchs, Gilad
AU - Ben-Shaul, Ido
AU - Mandelbrod, Matan
N1 - Publisher Copyright:
© 2022 Association for Computational Linguistics.
PY - 2022
Y1 - 2022
N2 - Identifying the release of new products and their predicted demand in advance is highly valuable for E-Commerce marketplaces and retailers. The information of an upcoming product release is used for inventory management, marketing campaigns and pre-order suggestions. Often, the announcement of an upcoming product release is widely available in multiple web pages such as blogs, chats or news articles. However, to the best of our knowledge, an automatic system to extract future product releases from web data has not been presented. In this work we describe an ML-powered multistage pipeline to automatically identify future product releases and rank their predicted demand from unstructured pages across the whole web. Our pipeline includes a novel Longformer-based model which uses a global attention mechanism guided by pre-calculated Named Entity Recognition predictions related to product releases. The model training data is based on a new corpus of 30K web pages manually annotated to identify future product releases. We made the dataset openly available at https://doi.org/10.5281/zenodo.6894770.
AB - Identifying the release of new products and their predicted demand in advance is highly valuable for E-Commerce marketplaces and retailers. The information of an upcoming product release is used for inventory management, marketing campaigns and pre-order suggestions. Often, the announcement of an upcoming product release is widely available in multiple web pages such as blogs, chats or news articles. However, to the best of our knowledge, an automatic system to extract future product releases from web data has not been presented. In this work we describe an ML-powered multistage pipeline to automatically identify future product releases and rank their predicted demand from unstructured pages across the whole web. Our pipeline includes a novel Longformer-based model which uses a global attention mechanism guided by pre-calculated Named Entity Recognition predictions related to product releases. The model training data is based on a new corpus of 30K web pages manually annotated to identify future product releases. We made the dataset openly available at https://doi.org/10.5281/zenodo.6894770.
UR - http://www.scopus.com/inward/record.url?scp=85152920745&partnerID=8YFLogxK
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AN - SCOPUS:85152920745
T3 - EMNLP 2022 - Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
SP - 273
EP - 281
BT - EMNLP 2022 - Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
PB - Association for Computational Linguistics (ACL)
T2 - 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022
Y2 - 7 December 2022 through 11 December 2022
ER -