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End-to-End Product Taxonomy Extension from Text Reviews

机译:文本评论的端到端产品分类扩展

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Product ontologies - consisting of a taxonomic categorization of product types and lists of attributes that types and products have - are invaluable for analyzing sales, opinions and ratings of items on e-commerce sites. Unfortunately, many international and smaller sites lack such ontologies, and instead feature only coarse high-level categories. We present a Siamese neural model which utilizes such coarse categories to learn a fine-grained hierarchical categorization of products, and jointly extract lists of product attributes from text reviews. We show that our model retains a high accuracy on the categorization task for unseen products and unseen category depths, and as a side effect learns to extract useful product attributes.
机译:产品本体-由产品类型的分类分类以及类型和产品具有的属性列表组成-对于分析电子商务网站上的商品的销售,观点和评级是非常宝贵的。不幸的是,许多国际和较小的站点都缺乏这种本体,而只具有粗糙的高级类别。我们提出了一个暹罗神经模型,该模型利用这种粗略的类别来学习产品的细粒度层次分类,并从文本评论中共同提取产品属性列表。我们表明,对于看不见的产品和看不见的类别深度,我们的模型在分类任务上保持了较高的准确性,并且作为副作用,我们学会了提取有用的产品属性。

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