automatic extraction of web data records containing user-generated content
In this paper, we are concerned with the problem of automatically extracting web data records that contain user-generated content (UGC). In previous work, web data records are usually assumed to be well-formed with a limited amount of UGC, and thus can be extracted by testing repetitive structure similarity. However, when a web data record includes a large portion of free-format UGC, the similarity test between records may fail, which in turn results in lower performance. In our work, we find that certain domain constraints (e.g., post-date ) can be used to design better similarity measures capable of circumventing the influence of UGC. In addition, we also use anchor points provided by the domain constraints to improve the extraction process, which ends in an algorithm called MiBAT (Mining data records Based on Anchor Trees). We conduct extensive experiments on a dataset consisting of forum thread pages which are collected from 307 sites that cover 219 different forum software packages. Our approach achieves a precision of 98.9\% and a recall of 97.3\% with respect to post record extraction. On page level, it perfectly handles 91.7\% of pages without extracting any wrong posts or missing any golden posts. We also apply our approach to comment extraction and achieve good results as well.
user-generated content information extraction structured data