KEA: practical automatic keyphrase extraction
Proceedings of the fourth ACM conference on Digital libraries
Improving browsing in digital libraries with keyphrase indexes
Decision Support Systems - From information retrieval to knowledge management: enabling technologies and best practices
Learning Algorithms for Keyphrase Extraction
Information Retrieval
KPSpotter: a flexible information gain-based keyphrase extraction system
WIDM '03 Proceedings of the 5th ACM international workshop on Web information and data management
Narrative text classification for automatic key phrase extraction in web document corpora
Proceedings of the 7th annual ACM international workshop on Web information and data management
Domain-specific keyphrase extraction
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Coherent keyphrase extraction via web mining
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Online shopping has been more and more popular nowadays. Online shopping starts with research and shopping research starts with search. In order to provide a more streamlined user experience in shopping related research, it is critical for e-commerce sites to accurately identify what a Web page is talking about. Concept extraction is a nice solution for this purpose. In this paper, we investigate two concept extraction methods: Automatic Concept Extractor (ACE) and Automatic Keyphrase Extraction (KEA). ACE is an unsupervised method that looks at both text and HTML tags. We upgrade ACE into Improved Concept Extractor (ICE) with significant improvements. KEA is a supervised learning system. It first builds a Naive Bayes model from training documents where concepts are manually assigned. The trained model is then used to automatically find concepts in new documents. In order to evaluate the two systems, we create a gold standard by manually assigning concepts to each page in the collection. We tune different parameters of ICE and KEA to generate concepts. And we use precision, recall and F1 to evaluate the concepts. The experimental results demonstrate that ICE significantly outperforms KEA in concept extraction for online shopping.