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Intent term selection and refinement in e-commerce queries
In e-commerce, a user tends to search for the desired product by issuing...
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Joint Intent Detection And Slot Filling Based on Continual Learning Model
Slot filling and intent detection have become a significant theme in the...
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Distant Supervision for E-commerce Query Segmentation via Attention Network
The booming online e-commerce platforms demand highly accurate approache...
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JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search
An accurate understanding of a user's query intent can help improve the ...
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Comparing Convolutional Neural Networks to Traditional Models for Slot Filling
We address relation classification in the context of slot filling, the t...
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Type-aware Convolutional Neural Networks for Slot Filling
The slot filling task aims at extracting answers for queries about entit...
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Distant-Supervised Slot-Filling for E-Commerce Queries
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 such labeled data, especially in e-commerce is expensive and time-consuming because the number of slots increases as new products are added. In this paper, we present distant-supervised probabilistic generative models, that require no manual annotation. The proposed approaches leverage the readily available historical query logs and the purchases that these queries led to, and also exploit co-occurrence information among the slots in order to identify intended product characteristics. We evaluate our approaches by considering 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 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.
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