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We study the problem of query attribute value extraction, which aims toidentify named entities from user queries as diverse surface form attributevalues and afterward transform them into formally canonical forms. Such aproblem consists of two phases: {named entity recognition (NER)} and {attributevalue normalization (AVN)}. However, existing works only focus on the NER phasebut neglect equally important AVN. To bridge this gap, this paper proposes aunified query attribute value extraction system in e-commerce search namedQUEACO, which involves both two phases. Moreover, by leveraging large-scaleweakly-labeled behavior data, we further improve the extraction performancewith less supervision cost. Specifically, for the NER phase, QUEACO adopts anovel teacher-student network, where a teacher network that is trained on thestrongly-labeled data generates pseudo-labels to refine the weakly-labeled datafor training a student network. Meanwhile, the teacher network can bedynamically adapted by the feedback of the student's performance onstrongly-labeled data to maximally denoise the noisy supervisions from the weaklabels. For the AVN phase, we also leverage the weakly-labeledquery-to-attribute behavior data to normalize surface form attribute valuesfrom queries into canonical forms from products. Extensive experiments on areal-world large-scale E-commerce dataset demonstrate the effectiveness ofQUEACO.

QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction