Abstract
Slot-filling refers to the task of annotating individual terms in a query with the corresponding intended product characteristics (product type, brand, gender, size, color, etc.). These characteristics can then be used by a search engine to return results that better match the query's product intent. Traditional methods for slot-filling require the availability of training data with ground truth slot-annotation information. However, generating representative labeled data, especially in big-data driven platforms like e-commerce is expensive and time consuming, given the volume and velocity of the data. In this paper, we present distant-supervised probabilistic generative models, that require no manual annotation. The proposed approaches leverage the readily available historical queries and their subsequent transaction logs, and also exploit co-occurrence information among the slots in order to identify intended product characteristics. We evaluate our approaches by considering both how they affect retrieval performance, as well as how well they classify the slots. In terms of retrieval, our approaches achieve better ranking performance (up to 156%) over Okapi BM25. Moreover, our approach that leverages co-occurrence information leads to better performance than the one that does not on both the retrieval and slot classification tasks.
Original language | English (US) |
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Title of host publication | Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021 |
Editors | Yixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 677-686 |
Number of pages | 10 |
ISBN (Electronic) | 9781665439022 |
DOIs | |
State | Published - 2021 |
Event | 2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States Duration: Dec 15 2021 → Dec 18 2021 |
Publication series
Name | Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021 |
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Conference
Conference | 2021 IEEE International Conference on Big Data, Big Data 2021 |
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Country/Territory | United States |
City | Virtual, Online |
Period | 12/15/21 → 12/18/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.