Abstract
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.
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© 2009 Springer-Verlag Berlin Heidelberg
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Raju, S., Pingali, P., Varma, V. (2009). An Unsupervised Approach to Product Attribute Extraction. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds) Advances in Information Retrieval. ECIR 2009. Lecture Notes in Computer Science, vol 5478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00958-7_88
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DOI: https://doi.org/10.1007/978-3-642-00958-7_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00957-0
Online ISBN: 978-3-642-00958-7
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