Table 7.

Comparing F₁-score of traditional models (Thai sentences dataset) with focusing POS tags (N+V+Adj+Det+Prep: NVADP).

		Rang of N-gram
		Micro $F_1$-score					Macro $F_1$-score
Dataset/Model		Unigram	Uni+ Bigram	Uni+ Trigram	Bigram	Trigram	Unigram	Uni+ Bigram	Uni+ Trigram	Bigram	Trigram
Original	MNB	0.6425	0.6704	0.6592	0.6648	0.5419	0.6375	0.6726	0.6585	0.6652	0.5344
	SVM	0.7374	0.7318	0.7318	0.6592	0.5363	0.7394	0.7361	0.7331	0.6583	0.5289
	LR	0.7095	0.7039	0.6927	0.6760	0.5307	0.7062	0.7007	0.6898	0.6758	0.5229
+all POS Tags	MNB	0.6760	0.6872	0.6480	0.6927	0.6089	0.6673	0.6827	0.6408	0.6855	0.5959
	SVM	0.7430	0.7374	0.7263	0.7095	0.6201	0.7430	0.7395	0.7253	0.7075	0.6088
	LR	0.7207	0.7318	0.6816	0.6760	0.5866	0.7188	0.7319	0.6741	0.6703	0.5681
+focusing Tags	MNB	0.6704	0.6816	0.6760	0.6816	0.5754	0.6626	0.6763	0.6728	0.6748	0.5634
	SVM	0.7598	0.7542	0.7386	0.6872	0.6145	0.7621	0.7560	0.7494	0.6867	0.6023
	LR	0.7486	0.7430	0.7374	0.6816	0.5698	0.7469	0.7409	0.7389	0.6794	0.5516
+focusing Words	MNB	0.6648	0.6704	0.6592	0.6536	0.5307	0.6647	0.6723	0.6602	0.6544	0.5260
	SVM	0.7263	0.7095	0.6927	0.6480	0.5307	0.7295	0.7123	0.6957	0.6471	0.5273
	LR	0.6983	0.7039	0.6872	0.6592	0.4972	0.6976	0.7015	0.6844	0.6574	0.4928