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Text mining for product attribute extraction
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From frequency to meaning: vector space models of semantics
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HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Template-based information extraction without the templates
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Fine-grained class label markup of search queries
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Bootstrapped named entity recognition for product attribute extraction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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Short listings such as classified ads or product listings abound on the web. If a computer can reliably extract information from them, it will greatly benefit a variety of applications. Short listings are, however, challenging to process due to their informal styles. In this paper, we present an unsupervised information extraction system for short listings. Given a corpus of listings, the system builds a semantic model that represents typical objects and their attributes in the domain of the corpus, and then uses the model to extract information. Two key features in the system are a semantic parser that extracts objects and their attributes and a listing-focused clustering module that helps group together extracted tokens of same type. Our evaluation shows that the semantic model learned by these two modules is effective across multiple domains.