#Semeval 2017 task3 how to

This will be reflected in our primary evaluation score, which only considers the top-10 results.Д®valuation: The official scorer will provide a number of evaluation measures to assess the quality of the output of a system (see the tools page), but the official evaluation measure towards which all systems will be evaluated and ranked will be MAP using the first 10 ranked comments only. We believe the user cares much less about what happens in lower positions (e.g., after the 10th) in the rank, as they typically do not ask for the next page of the next 10 comments. Thus, the users would like to have good comments to be concentrated in the first 10 positions, (i.e., all good comments ranked before any non-good comment). We will evaluate the position of good comments in the rank thus, this is again a ranking task.ДЄlthough, the systems are supposed to work on 100 comments, we take an application-oriented view in the evaluation: we assume that potential users are presented with a relatively short list of candidate answers (e.g., 10 as in common search engines today). We want the 'Good' comments to be ranked above the 'PotentiallyUseful' or 'Bad' comments, which will be considered just bad in terms of evaluation (the gold labels are contained in the RELC_RELEVANCE2ORGQ field of the related XML file). RerankВ the 100 comments (10 questions x 10 comments) according to their relevance with respect to the original question. The set of the first 10 related questions (retrieved by a search engine), each associated with its first 10 comments appearing in its thread, Subtask C: Question-External Comment Similarity - this is the main English subtask.ДЄ new question (aka the original question), The format required for the output of your systems will be detailed in the scorer and in the format-checker README files.

For each original question, you have to consider the 10 related questions associated with it. Note: also in this case, the dataset is formatted for training in this subtask. Again, this is not a classification task it is a ranking task.Д®valuation: As in subtask A, t he official scorer will provide a number of evaluation measures to assess the quality of the output of a system (see the tools page), but the official evaluation measure towards which all systems will be evaluated and ranked is MAP using the 10 ranked questions. See the README file for a detailed explanation of their meaning. The gold labels for this subtask are contained in the RELQ_RELEVANCE2ORGQ field of the XML file. In this case, we will consider the 'PerfectMatch' and 'Relevant' questions both as good (i.e., we will not distinguish between them and we will consider them both 'Relevant'), and they should be ranked above the 'Irrelevant' questions. Rerank the related questions according to their similarity with respect to the original question. The set of the first 10 related questions (retrieved by a search engine),

The format required for the output of your systems will be detailed in the scorer and format-checker README files.ДЄ new question (aka original question) and Note: the dataset is formatted for training in this subtask. Therefore,В this is a ranking task, not a classification task.Д®valuation: The official scorer provides a number of evaluation measures to assess the quality of the output of a system (see the tools page), but the official evaluation measure towards which all systems will be evaluated and ranked will be mean average precision (MAP) using the 10 ranked comments. Although we target the first 10 comments in terms of their time of postingВ (rather than in terms of their relevance), the output of the system will be a ranked list of comments (in terms of probability of relevance). See the datasets README file for a detailed description of the XML file format. The gold labels for this subtask are contained in the RELC_RELEVANCE2RELQ field of the XML files. We want the 'Good' comments to be ranked above the 'PotentiallyUseful' or 'Bad' comments the latter two will not be distinguished and will be considered 'Bad' in terms of evaluation. Rerank these 10 comments according to their relevance with respect to the question. Its first 10 comments in the question thread, Subtask A is the same as subtask A at SemEval-2015 Task 3, but with slightly different annotation and a different evaluation measure. There are three reranking subtasks associated with the English dataset.

#Semeval 2017 task3 how to

Note: For instructions on how to submit system results to any of these tasks, please check this page. Subtask E (English): Multi-Domain Duplicate Question Detection.Subtask D (Arabic): Rerank correct answers for a new Question.Subtask C (English): Question-External Comment Similarity.Subtask B (English): Question-Question Similarity.Subtask A (English): Question-Comment Similarity.