Experimental results of note classification with different feature generation strategies – Part 2

Strategy 5 : input + 12 feature types + weighted global features

Features M. et M. , « Aspects du droit dans marocain » , Droit et Société , , p. .ALLCAP INITIAL STARTINITIAL FIRSTCAP ALLSMALL ALLCAP INITIAL FIRSTCAP PUNC PUNC FIRSTCAP ALLSMALL ALLSMALL ALLSMALL ALLSMALL NONIMPCAP ALLSMALL PUNC PUNC ITALIC FIRSTCAP ITALIC ALLSMALL ITALIC FIRSTCAP PUNC ALLNUMBERS PUNC ALLSMALL POSSPAGE NUMBERS DASH PUNC
Feature counts m.:2 et:2 ,:4 «:1 aspects:1 du:1 droit:2 dans:1 marocain:1 »:1 société:1 p.:1 .:1 allcap:2 initial:2 strartintial:1 firstcap:5 allsmall:8 punc:7 nonimpcap:1 italic:3 posspage:1 allnumbers:1 numbers:1 dash:1
Numerical features 9:1 23:4 25:1 34:1 35:1 47:2 67:2 68:1 69:1 70:2 71:1 72:1 73:1 10001:3 10002:5 10003:2 10004:12 10005:8 10006:2 10007:7 10008:1 10009:1 10010:1 10011:1 10012:1
#Unique features 3949

Here we added 5 global features describing the distributional patterns of local features in a note string. Since a global feature expresses a pattern about features, it has a binary value (true or false). And we decide to weight the global features according to the local and global feature length, for more exact comparison in the vector space.

Total accuracy : 90.0

[Positive] Precision : 92.20 Recall : 94.84     [Negative] Precision : 82.18 Recall : 74.77

Not only total accuracy, the other accuracies also increased and especially the recall of negative notes that achieves about 5 point more than the best result until now (second strategy, negative recall: 69.37).

 

Strategy 6 : input + non-weighted global features

Features M. et M. , « Aspects du droit dans marocain » , Droit et Société , , p. .STARTINITIAL
Feature counts m.:2 et:2 ,:4 «:1 aspects:1 du:1 droit:2 dans:1 marocain:1 »:1 société:1 p.:1 .:1 strartintial:1
Numerical features 9:1 23:4 25:1 34:1 35:1 47:2 67:2 68:1 69:1 70:2 71:1 72:1 73:1 10001:1
#Unique features 3938

This time, we return to the problem of the strategy 3 that we leaved alone for a moment. Despite the failure of the local feature application, the global features based on them are successfully applied to the SVM classification. Expecting that there would be a better way to reorganize the local feature space, we first try to eliminate all the local features from the strategy 5. We need not weight the global features because the local features that caused the imbalance with the binary values of global feature are now disappeared.

Total accuracy : 90.65

[Positive] Precision : 92.5 Recall : 95.42     [Negative] Precision : 84.0 Recall : 75.68

We have obtained an interesting result by eliminating the local features. The performance is not really different compared to the previous experiment. And it means that the existing local feature usage does not influence the classification performance. It is quite interesting because when not applying the global features, the local ones have been rather negative for classification (strategy 3). In this circumstance we expect that a set of well-organized local features may increase the performance and at least it is not really negative to the performance. With this supposition, we try to apply some other representations of local features in the next posting.

 


Vous aimerez aussi...

Laisser un commentaire

Votre adresse de messagerie ne sera pas publiée. Les champs obligatoires sont indiqués avec *

Ce site utilise Akismet pour réduire les indésirables. En savoir plus sur comment les données de vos commentaires sont utilisées.