Biblio

Nowadays, a massive number of data, referred as big data, are being collected from social networks and Internet of Things (IoT), which are of tremendous value. Many deep learning-based methods made great progress in the extraction of knowledge of those data. However, the knowledge extraction of the law data poses vast challenges on the deep learning, since the law data usually contain the privacy information. In addition, the amount of law data of an institution is not large enough to well train a deep model. To solve these challenges, some privacy-preserving deep learning are proposed to capture knowledge of privacy data. In this paper, we review the emerging topics of deep learning for the feature learning of the privacy data. Then, we discuss the problems and the future trend in deep learning for privacy-preserving feature learning on law data.

The following article shows the precision, the recall and the F1-measure for three knowledge extraction methods under Open Information Extraction paradigm. These methods are: ReVerb, OLLIE and ClausIE. For the calculation of these three measures, a representative sample of Reuters-21578 was used; 103 newswire texts were taken randomly from that database. A big discrepancy was observed, after analyzing the obtained results, between the expected and the observed precision for ClausIE. In order to save the observed gap in ClausIE precision, a simple improvement is proposed for the method. Although the correction improved the precision of Clausie, ReVerb turned out to be the most precise method; however ClausIE is the one with the better F1-measure.