At an optimal decision threshold, however, both yielded a similar f-measure result.

Since both algorithms show different behaviour with increasing experience and PROMODES-H yields a higher f-measure across all datasets, we will investigate in the next experiments how these differences manifest themselves at the boundary level.

Our model outperforms a GIZA++ Model-4 baseline by 6.3 points in F-measure , yielding a 1.1 BLEU score increase over a state-of-the-art syntax-based machine translation system.

We treat word alignment as a parsing problem, and by taking advantage of English syntax and the hypergraph structure of our search algorithm, we report significant increases in both F-measure and BLEU score over standard baselines in use by most state-of-the-art MT systems today.

Note that Equation 2 is equivalent to maximizing the sum of the F-measure and model score of y:

These plots show the current F-measure on the training set as time passes.

The first three columns of Table 2 show the balanced F-measure , Precision, and Recall of our alignments versus the two GIZA++ Model-4 baselines.

(1) that using the structure of l-best English syntactic parse trees is a reasonable way to frame and drive our search, and (2) that F-measure approximately decomposes over hyperedges.

Neither recall nor f-measure are reported.

The feature whose omission causes the biggest drop in f-measure is set aside as a strong feature.

From the ranked list of features f1 to fn we evaluate increasingly extended feature sets f1.. fi for i = 2..n. We select the feature set that yields the best balanced performance, at 45.7% precision and 53.6% f-measure .

Comparing with TextRunner, WOEPOS runs at the same speed, but achieves an F-measure which is between 18% and 34% greater on three corpora; WOEparse achieves an F-measure which is between 72% and 91% higher than that of TextRunner, but runs about 30X times slower due to the time required for parsing.

Figure 3: WOEPOS achieves an F-measure , which is between 18% and 34% better than TextRunner’s.

Figure 4: WOEparse’s F-measure decreases more slowly with sentence length than WOEPOS and TextRunner, due to its better handling of difficult sentences using parser features.

Compared with TextRunner (the state of the art) on three corpora, WOE yields between 72% and 91% improved F-measure — generalizing well beyond Wikipedia.

As shown in the experiments on three corpora, WOEparse achieves an F-measure which is between 72% to 91% greater than TextRunner’s.

As shown in the experiments, WOEPOS achieves an improved F-measure over TextRunner between 18% to 34% on three corpora, and this is mainly due to the increase on precision.

Table 2: Precision, recall, and F-measure of three methods for detecting bilateral conversations in literary texts.

The precision and recall values shown for the baselines in Table 2 represent the highest performance we achieved by varying t between 0 and 1 (maximizing F-measure over 25).

Both baselines performed significantly worse in precision and F-measure than our quoted speech adjacency method for detecting conversations.

F-Measure (F): the harmonic mean of purity and inverse purity.

We use F-measure as the primary measure just liking WePSl and WePS2.

The F-Measure vs. 1 on three data sets

The coefficient Oi for the composite kernel are tuned with respect to F-measure (F) on the development set of HIT corpus.

Those thresholds are also tuned on the development set of HIT corpus with respect to F-measure .

The evaluation is conducted by means of Precision (P), Recall (R) and F-measure (F).