Evaluation Metrics. Given two monolingual corpora E and F , we sup- pose there exists a ground truth parallel corpus G and denote an extracted parallel corpus as D. The quality of an extracted parallel corpus can be mea- sured by F1 = 2|D ∩ G|/(|D| + |G|).
Evaluation Metrics. Evaluation is important for all NLP techniques, to assess to what extent the method is working. As in this project we are mainly dealing with the evaluation of NER, we will discuss the different evaluation metrics relevant to this technique Prediction True False Label True tp fn False fp tn Table 2.1: Illustrating the true/false positive/negative categories. and give examples within this context. Most metrics involve calculations of per- centages between correctly and incorrectly classified items. In the case of NER, we predict a label for each token. That predicted label is compared to the true label, and we can then put each prediction in one of the following categories: • True positive (tp). When a token is part of an entity, and the predicted label is the correct entity. • True negative (tn). When a token is not part of an entity, and the predicted label is also not part of an entity. • False negative (fn). When a token is part of an entity, but the predicted label is not part of an entity. More simply put: an entity that has not been recognised by the system. • False positive (fp). When a token is not part of an entity, but the predicted label is an entity. More simply put: the system recognises an entity where there is none. These categories are further illustrated in table 2.1. Once we have this in- formation, we can calculate some metrics. The most used measures in machine learning in general are recall, precision and F1 score, and these are almost always used to evaluate NER too. Recall is a measure that indicates out of all the entities in a text, what per- centage have been correctly labelled as an entity. It can also be viewed as the percentage of entities that have been found. It is defined as follows: + Recall = tp tp fn (2.1) Precision is a measure that indicates, out of all the labelled entities, what percentage has been assigned the correct label. In essence, this means that it shows that when an algorithm predicts an entity, how often it is right. It is defined as follows: + Precision = tp tp fp
Evaluation Metrics. Plan for evaluation of results are described in the table in Section 2.3. The quantitative metrics to be used in the assessment of results are: Time measurements of running the code generator. Time measurements of running the generated code. Calculate the cost of using the code generator at project end. Table 1: Quantitative metrics to be used in the assessment of results, for UC12.1
Evaluation Metrics. Plan for evaluation of results and quantitative metrics to be used in the assessment of results obtained by the use case: Post-processing and viewing of post-process is possible at the MRI scanner without hampering the low-latency processes and not disturbing the real-time control by operators. The demonstrator shall use a high-demanding application like DTI fiber tracking (see Figure 11) or compressed sensing. The dynamic analysis tool will run on a Windows platform and successfully detect software defects. The runtime analysis tool will enable high level partitioning decisions taking communication overhead in to account.
Evaluation Metrics. The main metrics of interest here can be identified as: Design development time o Intention is to have an as low as possible figure compared to current design times o Approximation of current design times will be provided Flexibility of the platform o To reflect the efforts to introduce or extract a tool from the chain User experiences o To reflect the benefits of users when working with the platform
Evaluation Metrics. Recent papers using the WebQuestions dataset have primarily used the average F1-score as the main evaluation metric, defined |Q| q as: avg F 1 = 1 ∈
Evaluation Metrics. Since many of the metrics applicable to evaluating the fellowship program extend beyond the time of the fellowship award, all Fellows will be identified and tracked throughout their graduate studies. All graduate students at Rensselaer undergo an annual review of academic progress, from which much of the student information can be gleaned. The PIs will generate an annual evaluation report prior to the selection of next year’s Fellow for the selection panel consideration. The first part of the evaluation report will consist of measures related to student quality. The report will quantify: - Time to completion of major doctoral program milestones (Department Qualifying Exam, Research Candidacy Exam, and Thesis Defense) - Success rate at doctoral program milestones - Number of journal publications submitted or accepted - Number of peer-reviewed conference papers or presentations - Professional awards received at different levels of recognitions - These measures will be compared to the values across the program at large. The evaluation report may also include qualitative analysis by faculty advisors regarding research quality and/or applicability to the design, construction, operation, and regulation of nuclear facilities and the safe handling of nuclear materials. The second part of the evaluation report will reflect measures of the student recruitment and enrollment. Measures will include: - Total number of applicants to the nuclear engineering graduate program - Fraction of applicants that meet the minimum GPA requirement for fellowship eligibility - Fraction of applicants that are US Citizens or permanent residents - Fraction of applicants that are women and minorities The evaluation report will be shared internally and externally periodically. Regular performance reports will be submitted to NRC on the required period basis.
Evaluation Metrics. We measure both parsing performance and inter- annotator agreement using tagging and parsing eval- uation metrics. This choice allows for a direct com- parison between parsing and agreement results. In this context, POS refers to tagging accuracy. We utilize the standard metrics Unlabeled Attachment Score (UAS) and Label Accuracy (LA) to measure accuracy of head attachment and dependency labels. We also utilize the standard parsing metric Labeled Attachment Score (LAS), which takes into account both dependency arcs and dependency labels. In all our parsing and agreement experiments, we exclude punctuation tokens from the evaluation.
Evaluation Metrics. For validation, precision, recall, and F1 scores are used to estimate the effectiveness of extraction by comparing the system predicted results (before human revision) and the ground truth.
