In this study, a numerical method was proposed to evaluate the calligraphy work called calligraphy evaluation system. Four classical chirographies of ＂Kaisho＂, ＂Gyosho＂, ＂Sousho＂ and ＂Hiragana＂, and 47 charact...In this study, a numerical method was proposed to evaluate the calligraphy work called calligraphy evaluation system. Four classical chirographies of ＂Kaisho＂, ＂Gyosho＂, ＂Sousho＂ and ＂Hiragana＂, and 47 characters for each chirography, were selected and analyzed by this system. The ＂Sumi＂ distribution of character was clarified from 12 directions and summarized into four parts of horizontal part, diagonal left part, vertical part and diagonal fight part. The character＇s contour line was converted to a signal data in order to calculate roundness index. The degree of character＇s radian was presented by roundness index. The smooth index was calculated at the same time. Additionally, width index, ＂Sumi＂ ratio, stability index also were calculated to contrast the features of each style. The main character points of four styles of ＂Kaisho＇, ＂Gyosho＂, ＂Sousho＂, ＂Hiragana＂ were extracted to compare each other, and provide a reference for learners. The learners could obtain the quantitative data to understand their work＇s characteristics. It can also be compared with other person＇s work by this system in order to improve learners＇ writing skill.展开更多
In this work, a hybrid method is proposed to eliminate the limitations of traditional protein-protein interactions （PPIs） extraction methods, such as pattern learning and machine learning. Each sentence from the bio...In this work, a hybrid method is proposed to eliminate the limitations of traditional protein-protein interactions （PPIs） extraction methods, such as pattern learning and machine learning. Each sentence from the biomedical literature containing a protein pair describes a PPI which is predicted by first learning syntax patterns typical of PPIs from training corpus and then using their presence as features, along with bag-of-word features in a maximum entropy model. Tested on the BioCreAtIve corpus, the PPIs extraction method, which achieved a precision rate of 64%, recall rate of 60%, improved the performance in terms of F1 value by 11% compared with the component pure pattern- based and bag-of-word methods. The results on this test set were also compared with other three extraction methods and found to improve the performance remarkably.展开更多
文摘In this study, a numerical method was proposed to evaluate the calligraphy work called calligraphy evaluation system. Four classical chirographies of ＂Kaisho＂, ＂Gyosho＂, ＂Sousho＂ and ＂Hiragana＂, and 47 characters for each chirography, were selected and analyzed by this system. The ＂Sumi＂ distribution of character was clarified from 12 directions and summarized into four parts of horizontal part, diagonal left part, vertical part and diagonal fight part. The character＇s contour line was converted to a signal data in order to calculate roundness index. The degree of character＇s radian was presented by roundness index. The smooth index was calculated at the same time. Additionally, width index, ＂Sumi＂ ratio, stability index also were calculated to contrast the features of each style. The main character points of four styles of ＂Kaisho＇, ＂Gyosho＂, ＂Sousho＂, ＂Hiragana＂ were extracted to compare each other, and provide a reference for learners. The learners could obtain the quantitative data to understand their work＇s characteristics. It can also be compared with other person＇s work by this system in order to improve learners＇ writing skill.
文摘In this work, a hybrid method is proposed to eliminate the limitations of traditional protein-protein interactions （PPIs） extraction methods, such as pattern learning and machine learning. Each sentence from the biomedical literature containing a protein pair describes a PPI which is predicted by first learning syntax patterns typical of PPIs from training corpus and then using their presence as features, along with bag-of-word features in a maximum entropy model. Tested on the BioCreAtIve corpus, the PPIs extraction method, which achieved a precision rate of 64%, recall rate of 60%, improved the performance in terms of F1 value by 11% compared with the component pure pattern- based and bag-of-word methods. The results on this test set were also compared with other three extraction methods and found to improve the performance remarkably.
