| 摘要(英) |
With the progress of information technologies, the traditional sheets of paper are replaced by web pages rapidly. The versatilities and abundant contents in the web pages make the extraction of useful information far more difficult than before. Information extraction technology has allowed us to extract such information from non-structural data by means of a series of processes, such as arrangement, distillation and coalition. Due to the potential changes of infra-structure of web pages and the diversities of designers’ personal styles, the most straight-forward but may not so cost effective way is to construct extraction system manually in accordance with the characteristics of individual web site. Therefore, automated extraction is the most wanted goal to achieve.
This thesis focuses on the extraction of conference information, such as conference names, locations, dates and accept paper dates, from DB World and international conference web pages. Since the bulletin-type conference web pages are not only text-rich but also written and published orally by different individuals without any structural harmonization, it makes the processes of integration and extraction rigorously. The system which is built on machine learning techniques is creditable and validated to perform well for the extraction of specific fields from cross web site pages. |
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