Error Classification | Using held-out validation data, we jointly tune the three parameters in the previous paragraph, Ci, 7%, and 25,, to optimize the F-score achieved by bi for error 61x3 However, an exact solution to this optimization problem is computationally expensive. |
Error Classification | Consequently, we find a local maximum by employing the simulated annealing algorithm (Kirkpatrick et al., 1983), altering one parameter at a time to optimize F-score by holding the remaining parameters fixed. |
Error Classification | Other ways we could measure our system’s performance (such as macro F-score ) would consider our system’s performance on the less frequent errors no less important than its performance on the |
Evaluation | To evaluate our thesis clarity error type identification system, we compute precision, recall, micro F-score, and macro F-score , which are calculated as follows. |
Evaluation | Then, the precision (Pi), recall (R1), and F-score (F1) for bi and the macro F-score (F) of the combined system for one test fold are calculated by 7510i 7510i 2PiRi A Z,- Fi |
Evaluation | However, the macro F-score calculation can be seen as giving too much weight to the less frequent errors. |
Evaluation Setup | First, following previous work, we evaluate our method using the labeled and unlabeled predicate-argument dependency F-score . |
Evaluation Setup | The dependency F-score captures both the target- |
Experiment and Analysis | For instance, there is a gain of 6.2% in labeled dependency F-score for HPSG formalism when 15,000 CFG trees are used. |
Experiment and Analysis | Across all three grammars, we can observe that adding CFG data has a more pronounced effect on the PARSEVAL measure than the dependency F-score . |
Experiment and Analysis | On the other hand, predicate-argument dependency F-score (Figure 5ac) also relies on the target grammar information. |
Implementation | This results in a drop on the dependency F-score by about 5%. |
Introduction | For instance, the model trained on 500 HPSG sentences achieves labeled dependency F-score of 72.3%. |
Introduction | Adding 15,000 Penn Treebank sentences during training leads to 78.5% labeled dependency F-score , an absolute improvement of 6.2%. |
Introduction | Experiments on the data from the Chinese tree bank (CTB-7) and Microsoft Research (MSR) show that the proposed model results in significant improvement over other comparative candidates in terms of F-score and out-of-vocabulary (OOV) recall. |
Method | The performance measurement indicators for word segmentation and POS tagging (joint S&T) are balance F-score , F = 2PIU(P+R), the harmonic mean of precision (P) and recall (R), and out-of-vocabulary recall (OOV—R). |
Method | It obtains 0.92% and 2.32% increase in terms of F-score and OOV—R respectively. |
Method | On the whole, for segmentation, they achieve average improvements of 1.02% and 6.8% in F-score and OOV—R; whereas for POS tagging, the average increments of F-sore and OOV—R are 0.87% and 6.45%. |
Related Work | Prior supervised joint S&T models present approximate 0.2% - 1.3% improvement in F-score over supervised pipeline ones. |
Abstract | The results using Reuters documents showed that the method was comparable to the current state-of-the-art biased-SVM method as the F-score obtained by our method was 0.627 and biased-SVM was 0.614. |
Conclusion | The results using the 1996 Reuters corpora showed that the method was comparable to the current state-of-the-art biased-SVM method as the F-score obtained by our method was 0.627 and biased-SVM was 0.614. |
Experiments | We empirically selected values of two parameters, “c” (tradeoff between training error and margin) and “j”, i.e., cost (cost-factor, by which training errors on positive examples) that optimized the F-score obtained by classification of test documents. |
Experiments | Figure 3 shows micro-averaged F-score against the 6 value. |
Experiments | F-score |
Experiment | F-score |
Experiment | Both the f-score and OOV—recall increase. |
Experiment | By comparing No-balance and ADD-N alone we can find that we achieve relatively high f-score if we ignore tag balance issue, while slightly hurt the OOV—Recall. |
INTRODUCTION | For example, the most widely used Chinese segmenter ”ICTCLAS” yields 0.95 f-score in news corpus, only gets 0.82 f-score on micro-blog data. |
Conclusion and outlook | We find that our Bigram model reaches 77% /t/-recovery F-score when run with knowledge of true word-boundaries and when it can make use of both the preceeding and the following phonological context, and that unlike the Unigram model it is able to learn the probability of /t/-deletion in different contexts. |
Conclusion and outlook | When performing joint word segmentation on the Buckeye corpus, our Bigram model reaches around above 55% F-score for recovering deleted /t/s with a word segmentation F-score of around 72% which is 2% better than running a Bigram model that does not model /t/-deletion. |
Experiments 4.1 The data | We evaluate the model in terms of F-score , the harmonic mean of recall (the fraction of underlying /t/s the model correctly recovered) and precision (the fraction of underlying /t/s the model predicted that were correct). |
Experiments 4.1 The data | Looking at the segmentation performance this isn’t too surprising: the Unigram model’s poorer token F-score , the standard measure of segmentation performance on a word token level, suggests that it misses many more boundaries than the Bigram model to begin with and, consequently, can’t recover any potential underlying /t/s at these boundaries. |
Experiments 4.1 The data | The generally worse performance of handling variation as measured by /t/-recovery F-score when performing joint segmentation is consistent with the finding of Elsner et al. |
Abstract | Our results show that our approach achieves 80.0% F-Score accuracy compared to an F-Score of 66.7% produced by a state-of-the-art semantic parser on a dataset of input format specifications from the ACM International Collegiate Programming Contest (which were written in English for humans with no intention of providing support for automated processing).1 |
Experimental Results | The two versions achieve very close performance (80% vs 84% in F-Score ), even though Full Model is trained with noisy feedback. |
Experimental Setup | Model | Recall ‘ Precision | F-Score ‘ |
Introduction | However, when trained using the noisy supervision, our method achieves substantially more accurate translations than a state-of-the-art semantic parser (Clarke et al., 2010) (specifically, 80.0% in F—Score compared to an F-Score of 66.7%). |
Introduction | The strength of our model in the face of such weak supervision is also highlighted by the fact that it retains an F-Score of 77% even when only one input example is provided for each input |
A UCCA-Annotated Corpus | We derive an F-score from these counts. |
A UCCA-Annotated Corpus | The table presents the average F-score between the annotators, as well as the average F-score when comparing to the gold standard. |
A UCCA-Annotated Corpus | An average taken over a sample of passages annotated by all four annotators yielded an F-score of 93.7%. |
Experiments | of our system that approximates the submodular objective function proposed by (Lin and Bilmes, 2011).7 As shown in the results, our best system8 which uses the hs dispersion function achieves a better ROUGE-1 F-score than all other systems. |
Experiments | (4) To understand the effect of utilizing syntactic structure and semantic similarity for constructing the summarization graph, we ran the experiments using just the unigrams and bigrams; we obtained a ROUGE-1 F-score of 37.1. |
Experiments | 7Note that Lin & Bilmes (2011) report a slightly higher ROUGE-1 score ( F-score 38.90) on DUC 2004. |
Experiments | To evaluate the parsing performance, we use the standard unlabeled (i.e., hierarchical spans) and labeled (i.e., nuclearity and relation) precision, recall and F-score as described in (Marcu, 2000b). |
Experiments | Table 2 presents F-score parsing results for our parsers and the existing systems on the two corpora.2 On both corpora, our parser, namely, lS-lS (TSP 1-1) and sliding window (TSP SW), outperform existing systems by a wide margin (p<7.le-05).3 On RST—DT, our parsers achieve absolute F-score improvements of 8%, 9.4% and 11.4% in span, nuclearity and relation, respectively, over HILDA. |
Experiments | On the Instructional genre, our parsers deliver absolute F-score improvements of 10.5%, 13.6% and 8.14% in span, nuclearity and relations, respectively, over the ILP-based approach. |
Experiment 3: Sense Similarity | Table 7: F-score sense merging evaluation on three hand-labeled datasets: OntoNotes (Onto), Senseval-2 (SE-2), and combined (Onto+SE-2). |
Experiment 3: Sense Similarity | For a binary classification task, we can directly calculate precision, recall and F-score by constructing a contingency table. |
Experiment 3: Sense Similarity | In addition, we show in Table 7 the F-score results provided by Snow et al. |
Evaluation | For four out of five conditions its F-score performance outperforms the baselines by 42-83%. |
Evaluation | These are the Most Frequent SCF (O’Donovan et al., 2005) which uniformly assigns to all verbs the two most frequent SCFs in general language, transitive (SUBJ-DOBJ) and intransitive (SUBJ) (and results in poor F-score ), and a filtering that removes frames with low corpus frequencies (which results in low recall even when trying to provide the maximum recall for a given precision level). |
Evaluation | The task we address is therefore to improve the precision of the corpus statistics baseline in a way that does not substantially harm the F-score . |
Experiments | The proposed method achieved about 44% recall and nearly 80% precision, outperforming all other systems in terms of precision, F-score and average precision8. |
Experiments | Table 4: Recall (R), precision (P), F-score (F) and average precision (aP) of the problem report recognizers. |
Experiments | Table 6: Recall (R), precision (P), F-score (F) and average precision (aP) of the problem-aid match recognizers. |
Experiment | We evaluated the performance ( F-score ) of our model on the three development sets by using different 04 values, where 04 is progressively increased in steps of 0.1 (0 < 04 < 1.0). |
Experiment | Table 2 shows the F-score results of word segmentation on CTB-5, CTB-6 and CTB-7 testing sets. |
Experiment | Table 2: F-score (%) results of five CWS models on CTB-5, CTB-6 and CTB-7. |