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

arXiv: https://arxiv.org/abs/2108.08468 · HTML: https://mlsanity.com/paper/2108.08468/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 61 references that are in the map.
- First posted on arXiv 19 Aug 2021.

## Builds on (most cited first, 30 of 30)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning](https://mlsanity.com/paper/1908.02983/) (2019)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [Robust Loss Functions under Label Noise for Deep Neural Networks](https://mlsanity.com/paper/1712.09482/) (2017)
- [Meta Pseudo Labels](https://mlsanity.com/paper/2003.10580/) (2020)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [Learning Dense Representations for Entity Retrieval](https://mlsanity.com/paper/1909.10506/) (2019)
- [BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision](https://mlsanity.com/paper/2006.15509/) (2020)
- [Scalable Zero-shot Entity Linking with Dense Entity Retrieval](https://mlsanity.com/paper/1911.03814/) (2019)
- [Automated Phrase Mining from Massive Text Corpora](https://mlsanity.com/paper/1702.04457/) (2017)
- [Learning Named Entity Tagger using Domain-Specific Dictionary](https://mlsanity.com/paper/1809.03599/) (2018)
- [OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]](https://mlsanity.com/paper/1806.01264/) (2018)
- [Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation](https://mlsanity.com/paper/1601.01343/) (2016)
- [Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning](https://mlsanity.com/paper/1910.14192/) (2019)
- [Unsupervised Cross-lingual Adaptation for Sequence Tagging and Beyond](https://mlsanity.com/paper/2010.12405/) (2020)
- [Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification](https://mlsanity.com/paper/1811.10999/) (2018)
- [Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data](https://mlsanity.com/paper/2106.08977/) (2021)
- [Attribute Extraction from Product Titles in eCommerce](https://mlsanity.com/paper/1608.04670/) (2016)
- [On Data Augmentation for Extreme Multi-label Classification](https://mlsanity.com/paper/2009.10778/) (2020)
- [Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoning](https://mlsanity.com/paper/2104.01666/) (2021)
- [An End-to-End Solution for Named Entity Recognition in eCommerce Search](https://mlsanity.com/paper/2012.07553/) (2020)
- [Entity Linking for Queries by Searching Wikipedia Sentences](https://mlsanity.com/paper/1704.02788/) (2017)
- [Entity Linking via Dual and Cross-Attention Encoders](https://mlsanity.com/paper/2004.03555/) (2020)

## Built on by (most cited first, 13 of 13)

- [WRENCH: A Comprehensive Benchmark for Weak Supervision](https://mlsanity.com/paper/2109.11377/) (2021)
- [ExtractGPT: Exploring the Potential of Large Language Models for Product Attribute Value Extraction](https://mlsanity.com/paper/2310.12537/) (2023)
- [Enhancing User Intent Capture in Session-Based Recommendation with Attribute Patterns](https://mlsanity.com/paper/2312.16199/) (2023)
- [A Unified Generative Approach to Product Attribute-Value Identification](https://mlsanity.com/paper/2306.05605/) (2023)
- [TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification](https://mlsanity.com/paper/2501.03835/) (2025)
- [Short Text Pre-training with Extended Token Classification for E-commerce Query Understanding](https://mlsanity.com/paper/2210.03915/) (2022)
- [Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2303.14898/) (2023)
- [Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels](https://mlsanity.com/paper/2307.01969/) (2023)
- [Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification](https://mlsanity.com/paper/2509.23874/) (2025)
- [A Framework for Leveraging Partially-Labeled Data for Product Attribute-Value Identification](https://mlsanity.com/paper/2405.10918/) (2024)
- [Query Brand Entity Linking in E-Commerce Search](https://mlsanity.com/paper/2502.01555/) (2025)
- [From Unstructured to Structured: LLM-Guided Attribute Graphs for Entity Search and Ranking](https://mlsanity.com/paper/2604.27410/) (2026)
- [TRACE: Agentic Catalog Enrichment with Multi-source Evidence Grounding](https://mlsanity.com/paper/2608.20844/) (2026)

## Abstract (arXiv)

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