A language model approach to keyphrase extraction
MWE '03 Proceedings of the ACL 2003 workshop on Multiword expressions: analysis, acquisition and treatment - Volume 18
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Red Opal: product-feature scoring from reviews
Proceedings of the 8th ACM conference on Electronic commerce
Semi-supervised learning of attribute-value pairs from product descriptions
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Semantic annotation of biosystematics literature without training examples
Journal of the American Society for Information Science and Technology
The role of query sessions in extracting instance attributes from web search queries
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
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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.