---
title: "Semi-Supervised Learning of Attribute-Value Pairs from Product Descriptions"
authors:
  - "Katharina Probst"
  - "Rayid Ghani"
  - "Marko Krema"
  - "Andrew E. Fano"
  - "Yan Liu"
doi: ""
url: "https://ijcai.org/proceedings/2007/456"
pdf: "https://ijcai.org/proceedings/2007/0456.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "2838-2842"
track: ""
keywords:
---

# Semi-Supervised Learning of Attribute-Value Pairs from Product Descriptions

Katharina Probst, Rayid Ghani, Marko Krema, Andrew E. Fano, Yan Liu

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 2838-2842.

## Abstract

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 (Naive 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.