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Retrieving Answers from Frequently Asked Questions Pages on the Web
| Content Provider | CiteSeerX |
|---|---|
| Abstract | We address the task of answering natural language questions by using the large number of Frequently Asked Questions (FAQ) pages available on the web. The task involves three steps: (1) fetching FAQ pages from the web; (2) automatic extraction of question/answer (Q/A) pairs from the collected pages; and (3) answering users ’ questions by retrieving appropriate Q/A pairs. Each step raises different scientific challenges. We discuss our solutions for each of the three tasks, and give detailed evaluation results on a collected corpus of about 3.6Gb of text data (293K pages, 2.8M Q/A pairs), with real users ’ questions sampled from a web search engine log. Specifically, we propose simple but effective methods for Q/A extraction and investigate several task-specific retrieval models for answering questions. Our best model finds answers for 36 % of the test questions in the top 20 results. Our overall conclusion is that FAQ pages on the web provide an excellent resource for addressing real users ’ information needs in a highly focused manner. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Question Page Faq Page Frequently Asked Question Several Task-specific Retrieval Model User Question Text Data Excellent Resource Test Question Different Scientific Challenge Web Search Engine Log Natural Language Question Collected Page Overall Conclusion Real User Question Effective Method Automatic Extraction Appropriate Pair Focused Manner Detailed Evaluation Result Question Answer Real User Information Collected Corpus |
| Content Type | Text |
