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.

我们提出了 QueryNER，一个手动注释的数据集和相应模型，用于电子商务查询分割。我们的研究工作关注的是将查询分成有意义的块，而不是仅仅提取产品标题或查询的特定方面。通过实验比较标记结果和令牌和实体删除方法来恢复无效和低召回的查询，我们展示了如何使用简单的数据增强技术使模型对噪声更加稳健。我们提供了 QueryNER 数据集的公开可用性。