C4.5: programs for machine learning
C4.5: programs for machine learning
Mining web snippets to answer list questions
AIDM '07 Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining - Volume 84
Web page classification: Features and algorithms
ACM Computing Surveys (CSUR)
The parallel path framework for entity discovery on the web
ACM Transactions on the Web (TWEB)
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List question answering (QA) offers a unique challenge in effectively and efficiently locating a complete set of distinct answers from huge corpora or the Web. In TREC-12, the median average F1 performance of list QA systems was only 6.9%. This paper exploits the wealth of freely available text and link structures on the Web to seek complete answers to list questions. We employ natural language parsing, web page classification and clustering to find reliable list answers. We also study the effectiveness of web page classification on both the recall and uniqueness of answers for web-based list QA.