# QueryNER: Segmentation of E-commerce Queries

arXiv: https://arxiv.org/abs/2405.09507 · HTML: https://mlsanity.com/paper/2405.09507/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 15 May 2024.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models](https://mlsanity.com/paper/2109.05620/) (2021)
- [SeqScore: Addressing Barriers to Reproducible Named Entity Recognition Evaluation](https://mlsanity.com/paper/2107.14154/) (2021)
- [Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning](https://mlsanity.com/paper/2005.11075/) (2020)

## Built on by (most cited first, 3 of 3)

- [Investigating LLM Applications in E-Commerce](https://mlsanity.com/paper/2408.12779/) (2024)
- [BroadGen: A Framework for Generating Effective and Efficient Advertiser Broad Match Keyphrase Recommendations](https://mlsanity.com/paper/2505.19164/) (2025)
- [IntentTune: Using user demand and personalization to resolve "unknown" query intents for e-commerce search](https://mlsanity.com/paper/2607.01530/) (2026)

## Abstract (arXiv)

We present QueryNER, a manually-annotated dataset and accompanying model for e-commerce query segmentation. Prior work in sequence labeling for e-commerce has largely addressed aspect-value extraction which focuses on extracting portions of a product title or query for narrowly defined aspects. Our work instead focuses on the goal of dividing a query into meaningful chunks with broadly applicable types. We report baseline tagging results and conduct experiments comparing token and entity dropping for null and low recall query recovery. Challenging test sets are created using automatic transformations and show how simple data augmentation techniques can make the models more robust to noise. We make the QueryNER dataset publicly available.