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Table 5.8. Results when Learning from Others Domain.Others Domain - Computer Domain1 1+2 1+2+3 patternF1 0.90/0.52 0.95/0.60 0.97/0.56 0.95/0.64F2 0.88/0.51 0.92/0.44 0.94/0.37 0.94/0.47F3 0.90/0.46 0.95/0.48 0.97/0.41 0.96/0.49F4 0.80/0.33 0.79/0.58 0.79/0.55 0.79/0.59F5 0.83/0.51 0.85/0.54 0.88/0.51 0.87/0.53F6 0.81/0.51 0.90/0.56 0.94/0.51 0.89/0.56ferences in performance are not as salient as differences in numbers of features.Incorporating verbal nouns into the categorization slightly improved the results.However, the patterns did not work in this task. The same experiment-switchingthe roles of the two list sets, the Computer and the Others domain, was thenperformed (see Tables 5.7 and 5.8).Along with adding N-grams, the recall became worse for the group of contentwords. In contrast, the group of function words showed better performance inthe recall, and the overall balance of precision and recall were well-performed.Calculating the F-measure with formula 5.1, in most evaluations of open domain,the functional group overtook the content group. This deviation is more salientin the Others domain. In the results of both the Computer domain and the Othersdomain, the model trained with functions performed better than the modeltrained with content. The function words in Japanese characterize the descriptivestyle of the text, meaning that this result shows a possibility of the acquisitionof various procedural expressions. From another perspective, when trigram wasadded as a feature, performance took decreased in recall. Adding the patterns,however, improved performance. It is assumed that there are dependencies betweenwords at a distance greater than three words, which is beneficial in theircategorization. Table 5.9 compares the results of SVM and j48.j48 decision tree.Table 5.10 lists the effectiveness of mutual information filtering.In both tables, values show the F-measure calculated with formula 5.1. Accordingto Table 5.9, SVM overtook j48.j48 overall. j48.j48 scarcely changes with76
Table 5.9. Comparison of SVM and Decision Tree.1 1+2 1+2+3SVM j48 SVM j48 SVM j48 #featureF3 0.84 0.79 0.84 0.83 0.84 0.83 3000.85 0.76 0.85 0.81 0.84 0.82 5000.84 0.76 0.86 0.82 0.86 0.83 10000.87 0.76 0.87 0.82 0.87 0.83 5000F5 0.84 0.79 0.84 0.82 0.82 0.81 3000.85 0.80 0.85 0.81 0.83 0.82 5000.86 0.80 0.86 0.81 0.84 0.81 10000.84 0.80 0.86 0.82 0.87 0.82 5000Table 5.10. Results of Pattern Selection with Mutual Information Filtering.100 300 500 no-filterComputer F3 0.53 0.53 0.53 0.52- Others F5 0.53 0.52 0.50 0.53Others F3 0.74 0.74 0.75 0.65- Computer F5 0.75 0.76 0.77 0.66an increase in the number of features, however, SVM gradually improves performance.For mutual information filtering, SVM marked the best results withno-filter in the Computer domain. However, in the case of learning from theOthers domain, the mutual information filtering appears effective.5.9 DiscussionThe comparison of SVM and decision tree shows the high degree of generalizationof SVM in a high dimensional feature space. From the results of mutual informationfiltering, I can recognize that the simple methods of other pre-cleaning arenot notably effective when learning from documents of the same domain. However,the simple methods work well in my task when learning from documents77
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Page 102 and 103: [29] Yoav Freund and Robert E. Scha
Page 104 and 105: [47] Chiori Hori, Takaaki Hori, Hid
Page 106 and 107: [63] Mingzhe Jin. Authorship attrib
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Page 112 and 113: [122] Akihiro Shinmori, Manabu Okum
Page 114 and 115: [141] Akihiro Tamura, Hiroya Takamu
Page 116 and 117: [161] Yudong Yang and HongJiang Zha
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