Advantage of the new categories:

Is such knowledge interesting or useful? One simple experiment can be carried out, using this information in order to compute a new baseline. As for the preceding baseline, each word is categorized into its most frequent category. The baseline reaches now a F1-score of 86%, an improvement of 6%.

Table 6 well illustrates the advantages of the new subcategories. Let us take the element JJ. Its default category is I (84%) in the old categorization. In the new categorization, its default category is left adjunct (AL) with a very high accuracy (99%). It is then really easier to categorize it. In other words, an adjective belongs to an NP if it occurs before a noun (with 1% of exceptions).

Table 6: Distribution of some elements in the old and new categories.

TAG	O	B-NP	I-NP	I
				NU	AL	AR	O	B+
PRP	0%	9%	91%	100%	-		0%	100%
NN	2%	17%	81%	98%	-		2%	3%
JJ	15%	1%	84%	-	99%	-	1%	24%
VBG	87%	6%	10%	-	22%	-	78%	3%

Another advantage of this categorization concerns the breaker problem. In the new categorization, the break is a property that each category can have, and it is not a competing categorization. This corresponds to the following view: One problem is the membership of an element to a given structure (categorization into NU or A). The second problem is to determine whether an element is a breaker or not. The two facts are not competing as they are in the IOB categorization: if an element is a breaker (tagged B in the old categorization), it has to belong to the structure. The tag PRP offers a good example. In the old categorization, it is mostly tagged I (91%), and sometimes B. In the new, it is always tagged N, and always has the break property. The 9% of the tag B correspond to the 9% where the preceding element is an NP. Two rules are then sufficient where a dozen were required with the old categorization.