Product Attribute Extraction is the task of automatically discovering attributes of products from text descriptions. In this paper, we propose a new approach which is both unsupervised and domain independent to extract the attributes. With our approach, we are able to achieve 92% precision and 62% recall in our experiments. Our experiments with varying dataset sizes show the robustness of our algorithm. We also show that even a minimum of 5 descriptions provide enough information to identify attributes. © Springer-Verlag Berlin Heidelberg 2009.
CITATION STYLE
Raju, S., Pingali, P., & Varma, V. (2009). An unsupervised approach to product attribute extraction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5478 LNCS, pp. 796–800). https://doi.org/10.1007/978-3-642-00958-7_88
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