Abstract
In this work, we address the brand entity linking problem for e-commerce search queries. The entity linking task is done by either i)a two-stage process consisting of entity mention detection followed by entity disambiguation or ii) an end-to-end linking approaches that directly fetch the target entity given the input text. The task presents unique challenges: queries are extremely short (averaging 2.4 words), lack natural language structure, and must handle a massive space of unique brands. We present a two-stage approach combining named-entity recognition with matching, and a novel end-to-end solution using extreme multi-class classification. We validate our solutions by both offline benchmarks and the impact of online A/B test.
Abstract (translated)
在这项工作中，我们解决了电子商务搜索查询中的品牌实体链接问题。实体链接任务可以通过以下两种方式完成：i) 一个两阶段的过程，包括实体提及检测后进行实体消歧；ii) 直接根据输入文本获取目标实体的端到端链接方法。该任务面临独特的挑战：查询非常短（平均2.4个词），缺乏自然语言结构，并且必须处理大量的独特品牌空间。我们提出了一种结合命名实体识别与匹配的两阶段方法，以及一种使用极端多类分类的新型端到端解决方案。我们通过离线基准测试和在线A/B测试的影响来验证我们的解决方案的有效性。
URL
https://arxiv.org/abs/2502.01555
