This paper addresses the task of extending a given synset with additional synonyms taking into account synonymy strength as a fuzzy value. Given a mono/multilingual synset and a threshold (a fuzzy value [0-1]), our goal is to extract new synonyms above this threshold from existing lexicons. We present twofold contributions: an algorithm and a benchmark dataset. The dataset consists of 3K candidate synonyms for 500 synsets. Each candidate synonym is annotated with a fuzzy value by four linguists. The dataset is important for (i) understanding how much linguists (dis/)agree on synonymy, in addition to (ii) using the dataset as a baseline to evaluate our algorithm. Our proposed algorithm extracts synonyms from existing lexicons and computes a fuzzy value for each candidate. Our evaluations show that the algorithm behaves like a linguist and its fuzzy values are close to those proposed by linguists (using RMSE and MAE). The dataset and a demo page are publicly available at https://portal.sina.birzeit.edu/synonyms.

翻译：本文针对在给定同义词集（synset）中根据同义强度（模糊值）扩展新同义词的任务展开研究。给定单语/多语同义词集及阈值（模糊值范围[0-1]），我们的目标是从现有词典中提取高于该阈值的新同义词。我们提出双重贡献：一种算法与一个基准数据集。该数据集包含500个同义词集的3000个候选同义词，每个候选词由四位语言学家标注模糊值。该数据集的重要性在于：（一）理解语言学家在同义词判定上的一致/分歧程度，（二）将其作为评估我们算法的基线。提出的算法从现有词典中提取同义词，并为每个候选词计算模糊值。评估结果表明，该算法的行为与语言学家高度一致，其模糊值（通过均方根误差RMSE和平均绝对误差MAE衡量）与语言学家标注值接近。数据集及演示页面已公开于 https://portal.sina.birzeit.edu/synonyms。