Semi-Supervised Learning of Attribute-Value Pairs from Product Descriptions
نویسندگان
چکیده
We describe an approach to extract attribute-value pairs from product descriptions. This allows us to represent products as sets of such attribute-value pairs to augment product databases. Such a representation is useful for a variety of tasks where treating a product as a set of attribute-value pairs is more useful than as an atomic entity. Examples of such applications include product recommendations, product comparison, and demand forecasting. We formulate the extraction as a classification problem and use a semi-supervised algorithm (co-EM) along with (Naı̈ve Bayes). The extraction system requires very little initial user supervision: using unlabeled data, we automatically extract an initial seed list that serves as training data for the supervised and semi-supervised classification algorithms. Finally, the extracted attributes and values are linked to form pairs using dependency information and co-location scores. We present promising results on product descriptions in two categories of sporting goods.
منابع مشابه
Semi-Supervised Learning to Extract Attribute-Value Pairs from Product Descriptions on the Web
We describe an approach to extract attribute-value pairs from product descriptions on the Web. The goal is to augment product databases by representing each product as a set of such attribute-value pairs. Such a representation is useful for a variety of tasks where treating the product as a set of attribute-value pairs is more useful than as an atomic entity. Examples include product recommenda...
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