俄语语义相关性的人机判断
Human and Machine Judgements for Russian Semantic Relatedness
摘要：
术语的语义相关性通过数值分数表示含义的相似性。一方面，人类很容易对语义相关性做出判断。另一方面，这种信息在语言处理系统中是有用的。虽然语义相关性已被广泛研究英语使用大量的语言资源，如联想规范，人类的判断，并从词汇数据库生成的数据集，没有这种评估资源已可用于俄罗斯的日期。我们的贡献解决了这个问题。我们提出了五个不同规模和目的的俄语语义相关性的语言资源，每个都是一个三元组（词_i，词_j，相关性_ij）的列表。其中四个是专为评估系统的计算语义相关性，相互补充的语义关系类型，他们代表。这些基准被用来组织一个关于俄语语义相关性的共享任务，吸引了19个团队。我们使用在这次比赛中确定的最好的方法之一，以产生第五个高覆盖率的资源，第一个开放的分布式俄语词库。对该词库的多次评估，包括一项涉及母语人士的大规模众包研究，表明其准确性很高。
英文摘要：
Semantic relatedness of terms represents similarity of meaning by a numerical score. On the one hand, humans easily make judgments about semantic relatedness. On the other hand, this kind of information is useful in language processing systems. While semantic relatedness has been extensively studied for English using numerous language resources, such as associative norms, human judgments, and datasets generated from lexical databases, no evaluation resources of this kind have been available for Russian to date. Our contribution addresses this problem. We present five language resources of different scale and purpose for Russian semantic relatedness, each being a list of triples (word_i, word_j, relatedness_ij). Four of them are designed for evaluation of systems for computing semantic relatedness, complementing each other in terms of the semantic relation type they represent. These benchmarks were used to organize a shared task on Russian semantic relatedness, which attracted 19 teams. We use one of the best approaches identified in this competition to generate the fifth high-coverage resource, the first open distributional thesaurus of Russian. Multiple evaluations of this thesaurus, including a large-scale crowdsourcing study involving native speakers, indicate its high accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
