BLANC for Imperfect Response Mentions | and we propose to extend the coreference F-measure and non-coreference F-measure as follows. |
BLANC for Imperfect Response Mentions | Coreference recall, precision and F-measure are changed to: |
BLANC for Imperfect Response Mentions | Non-coreference recall, precision and F-measure are changed to: |
Introduction | It calculates recall, precision and F-measure separately on coreference and non-coreference links in the usual way, and defines the overall recall, precision and F-measure as the mean of the respective measures for coreference and non-coreference links. |
Original BLANC | BLANC-gold solves this problem by averaging the F-measure computed over coreference links and the F-measure over non-coreference links. |
Original BLANC | Using the notations in Section 2, the recall, precision, and F-measure on coreference links are: |
Original BLANC | Similarly, the recall, precision, and F-measure on non-coreference links are computed as: |
Abstract | Experimental results show that the proposed model achieves 83% in F-measure , and outperforms the state-of-the-art baseline by over 7%. |
Conclusions and Future Work | Experimental results show our proposed framework outperforms the state-of-the-art baseline by over 7% in F-measure . |
Experiments | It is worth noting that the F-measure reported for the event phrase extraction is only 64% in the baseline approach (Ritter et al., 2012). |
Experiments | Method Tuple Evaluated Precision Recall F-measure |
Experiments | Method Tuple Evaluated Precision Recall F-measure |
Introduction | We have conducted experiments on a Twitter corpus and the results show that our proposed approach outperforms TwiCal, the state-of-the-art open event extraction system, by 7.7% in F-measure . |
Experimental Evaluation | leading to an F-measure of 53% for our initialization heuristic. |
Experimental Evaluation | on various quality metrics, of which F-Measure is typically considered most important. |
Experimental Evaluation | Our pure-LM13 setting (i.e., A = l) was seen to perform up to 6 F-Measure points better than the pure-TM14 setting (i.e., A = 0), whereas the uniform mix is seen to be able to harness both to give a 1.4 point (i.e., 2.2%) improvement over the pure-LM case. |
Experiments | Evaluation Metrics: We select precision(P), recall(R) and f-measure (F) as metrics. |
Experiments | The experimental results are shown in Table 2, 3, 4 and 5, where the last column presents the average F-measure scores for multiple domains. |
Experiments | Due to space limitation, we only show the F-measure of CR_WP on four domains. |
Introduction | Experimental results show that our method can achieve a 5.4% F-measure improvement over the traditional convolution tree kernel based method. |
Introduction | The overall performance of CTK and FTK is shown in Table 1, the F-measure improvements over CTK are also shown inside the parentheses. |
Introduction | FTK on the 7 major relation types and their F-measure improvement over CTK |
Experiments | Evaluation Metrics: We evaluate the proposed method in terms of precision(P), recall(R) and F-measure (F). |
Experiments | Figure 5 shows the performance under different N, where the F-Measure saturates when N equates to 40 and beyond. |
Experiments | F-Measure |
Evaluation | We measured correction performance by recall, precision, and F-measure . |
Evaluation | The simple error case frame-based method achieves an F-measure of 0.189. |
Evaluation | The hybrid methods achieve the best performances in F-measure . |
Empirical Evaluation | Since the purpose of solving this problem is to identify the threads which should be brought to the notice of the instructors, we measure the performance of our models using F-measure of the positive class. |
Empirical Evaluation | F-measure |
Empirical Evaluation | 6 shows 10-fold cross validation F-measure of the positive class for LR when different types of features are excluded from the full set. |