From the table we observe that the best result is obtained when k is 100.

Similarly when we deal with all the words in the corpus (17,465 words), we got best results when the words are clustered into 1100 clusters.

the best result is obtained when important words for two preceding and two following positions (defined in Section 4.3) are selected.

While experimenting with static word features, we have observed that a window of previous and next two words (wi_2...wi+2) gives best result (69.09) using the word features only.

The value of k (number of clusters) was varied till the best result is obtained.

means that the best configuration for PP-attachment does not always produce the best results for parsing

The SFU representation produces the best results for Bikel (F-score 0.010 above baseline), while for Charniak the best performance is obtained with word+SF (F-score 0.007 above baseline).

For both parsers the best results are achieved with SFU, which was also the best configuration for parsing with Bikel.

Comparing the semantic representations, the best results are achieved with SFU, as we saw in the gold-standard PP-attachment case.

the achievements of four different standard ML methods, to the goal of achieving the best results , as opposed to the other systems that mainly focused on one ML method, each.

Specifically, the AlWC_osWC feature variant achieves the best result with 87.75% accuracy.

Table 2 shows that SVM achieved the best result with 96.09% accuracy.