| Clustering Methods, Evaluation Metrics and Experimental Setup | Feature maximisation is a cluster quality metric which associates each cluster with maximal features i.e., features whose Feature F-measure is maximal. |
| Clustering Methods, Evaluation Metrics and Experimental Setup | Feature F-measure is the harmonic mean of Feature Recall and Feature Precision which in turn are defined as: |
| Clustering Methods, Evaluation Metrics and Experimental Setup | represents the weight of the feature f for element :10 and FC designates the set of features associated with the verbs occuring in the cluster c. A feature is then said to be maximal for a given cluster iff its Feature F-measure is higher for that cluster than for any other cluster. |
| Introduction | Their approach achieves a F-measure of 55.1 on 116 verbs occurring at least 150 times in Lexschem. |
| Introduction | The best performance is achieved when restricting the approach to verbs occurring at least 4000 times (43 verbs) with an F-measure of 65.4. |
| Introduction | We show that the approach yields promising results ( F-measure of 70%) and that the clustering produced systematically associates verbs with syntactic frames and thematic grids thereby providing an interesting basis for the creation and evaluation of a Verbnet-like classification. |
| Data and task | For Bulgarian, the F-measure of the full model is 92.8 compared to the best baseline result of 83.2. |
| Data and task | Within the semi-CRF model, the contribution of English sentence context was substantial, leading to 2.5 point increase in F-measure for Bulgarian (92.8 versus 90.3 F—measure), and 4.0 point increase for Korean (91.2 versus 87.2). |
| Data and task | Preliminary results show performance of over 80 F-measure for such monolingual models. |
| Introduction | Our results show that the semi-CRF model improves on the performance of projection models by more than 10 points in F—measure, and that we can achieve tagging F-measure of over 91 using a very small number of annotated sentence pairs. |
| Abstract | We transform tagged character sequences to word segmentations first, and then evaluate word segmenta-tions by F-measure , as defined in Section 5.2. |
| Abstract | We focus on the task of word segmentation only in this work and show that a comparable F-measure is achievable in a much more efficient manner. |
| Abstract | The addition of these features makes a moderate improvement on the F-measure , from 0.974 to 0.975. |