Removing spam from web corpora through supervised learning using FastText
| Authors | |
|---|---|
| Year of publication | 2017 |
| Type | Article in Proceedings |
| MU Faculty or unit | |
| Citation | |
| Web | Sborník konference |
| Keywords | Text corpora;Web spam;Supervised learning;FastText |
| Description | Unlike traditional text corpora collected from trustworthy sources, the content of web based corpora has to be filtered. This study briefly discusses the impact of web spam on corpus usability and emphasizes the importance of removing computer ge- nerated text from web corpora. The paper also presents a keyword com- parison of an unfiltered corpus with the same collection of texts cleaned by a su- pervised classifier trained using FastText. The classifier was able to recognise 71 % of web spam documents similar to the training set but lacked both precision and recall when applied to short texts from another data set. |
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