By Petra Perner
This publication constitutes the refereed complaints of the 14th commercial convention on Advances in facts Mining, ICDM 2014, held in St. Petersburg, Russia, in July 2014. The sixteen revised complete papers provided have been conscientiously reviewed and chosen from quite a few submissions. the subjects variety from theoretical features of knowledge mining to purposes of information mining, corresponding to in multimedia info, in advertising, in drugs and agriculture and in approach regulate, and society.
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Extra info for Advances in Data Mining. Applications and Theoretical Aspects: 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia, July 16-20, 2014. Proceedings
11, 12]. The following features were implemented for training of classifiers: tokens, URL elements, structural patterns, association of abbreviations with full words combinations. SVM and MEM were used for classifier training. 42 I. Kotenko et al. Among relevant publications that explore aspects of SVM for web page classification we also mark works of Joachims , Dumais et al.  and Yang et al. . Yang et al. also apply kNN, used by Calado et al.  and Lam et al. . More information about applying NB can be found in the works by already mentioned Joachims , Dumais et al.
Its essence lies in the definition of the category of the classified web page based on analyzing links that other web pages make to this one. Calculation of relation level of web pages containing links to a particular category is done. The classification is performed by nine categories. In general, it should be noted that most often used features that are applied for web page classification are extracted from the page text content. For instance, Dumais and Chen  separated concepts of web page text, header information and descriptive information service tag “meta”.
Fig. 6. Comparision between the performance of several supervised, semi-supervised and unsupervised methods, where our unsupervised method, UNERD, outperforms S2, S3 and S4 when trained on a small amount of annotated data, that outperform the mere dictionary-lookup (S1) 7 Conclusion and Future Work The unsupervised use of dictionary-lookup is known to enhance NER, however dictionaries have limitations for being finite and ambiguous. On the other hand, supervised NER such as Stanford’s NER Classifier that we tested here is known to perform very well but only with the availability of huge amounts of manually annotated training data that is very costly, time consuming and sometimes inaccurate due to inter-annotator inconcistencies.