解决电商搜索中短文本品牌实体链接难题，提升搜索准确率。
Query Brand Entity Linking in E-Commerce Search
- 分两阶段：先识别品牌词，再匹配正确品牌实体。
- 提出端到端极端多分类模型，直接从短查询定位品牌。
- 在真实线上测试中显著提升搜索点击率，适合电商搜索优化者。
本文针对电商搜索中的品牌实体链接问题展开研究。由于查询极短（平均2.4个词）、缺乏自然语言结构且品牌数量庞大，传统方法面临挑战。我们提出两种方案：一种是先进行命名实体识别再匹配的两阶段方法；另一种是基于极端多分类的端到端新模型。通过离线基准测试和线上A/B实验验证，新方法在品牌链接准确率和搜索效果上均有显著提升。
原文摘要 · Abstract (English)
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.
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