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.
翻译:本研究针对电子商务搜索查询中的品牌实体链接问题展开探讨。实体链接任务可通过两种方式实现:i) 包含实体提及检测与实体消歧的两阶段流程;ii) 基于输入文本直接获取目标实体的端到端链接方法。该任务面临独特挑战:查询文本极短(平均2.4词)、缺乏自然语言结构,且需处理海量独立品牌空间。我们提出了一种结合命名实体识别与匹配的两阶段方法,以及采用极端多分类技术的新型端到端解决方案。通过离线基准测试与在线A/B测试的实际影响,我们对所提方案进行了双重验证。