Learning Algorithms for Keyphrase Extraction
Information Retrieval
Finding advertising keywords on web pages
Proceedings of the 15th international conference on World Wide Web
Domain-specific keyphrase extraction
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Representation models for text classification: a comparative analysis over three web document types
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
Extracting keyphrase set with high diversity and coverage using structural SVM
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Exploratory class-imbalanced and non-identical data distribution in automatic keyphrase extraction
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Can back-of-the-book indexes be automatically created?
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Integrating semantic relatedness and words' intrinsic features for keyword extraction
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Today, a huge amount of text is being generated for social purposes on social networking services on the Web. Unlike traditional documents, such text is usually extremely short and tends to be informal. Analysis of such text benefit many applications such as advertising, search, and content filtering. In this work, we study one traditional text mining task on such new form of text, that is extraction of meaningful keywords. We propose several intuitive yet useful features and experiment with various classification models. Evaluation is conducted on Facebook data. Performances of various features and models are reported and compared.