Constructing an Enhanced Version of WordNet

WordNet是自然语言处理和人工智能等领域研究的重要词汇资源。这个项目启动了WordNet的一个彻底增强版的开发。构建WordNet+涉及经验方法的新颖组合：人类注释，语料库分析和机器学习。WordNet+特别解决了WordNet有限的识别词义的能力，源于同义词集（“synsets”）之间布尔弧的稀疏性。首先，将量化的、定向的弧添加到5000个同义词集的核心集合中。这些弧线反映了唤起——一个同义词集的意义能在多大程度上引起另一个同义词集的联想。在选定核心同义词集之后，将从注释器中抽取25万个arc的随机子集。注释者经过培训和内部可靠性测试，使用专门设计和测试的界面记录他们心理关联的强度。剩余的弧线将使用机器学习算法从手动获得的弧线中推断出来。所有结果将提供给研究界：核心概念、间接共现矩阵和所有可用的评级。鉴于WordNet过去对许多不同学科的贡献，这个研究工具构建的初始阶段应该会引起极大的兴趣，并对相关工作产生重大影响。

WordNet is an important lexical resource for research in areas including NLP and AI. This project initiates the development of a radically enhanced version of WordNet. Constructing WordNet+ involves a novel combination of empirical methods: human annotation, corpus analysis, and machine learning. WordNet+ specifically addresses some of WordNet's limited ability to identify word senses, stemming from the sparsity of Boolean arcs among sets of synonymous words ("synsets"). First, quantified, oriented arcs are to be added among a core set of 5,000 synsets. These arcs reflect evocation--the extent to which the meaning of one synset brings to mind another. Following the selection of the core synsets, a random subset of 250,000 arcs are to be elicited from annotators. The annotators, trained and tested for inter- and intra-reliability, record the strength of their mental associations using a specially designed and tested interface. The remaining arcs are to be extrapolated from the manually obtained arcs using machine learning algorithms.All results will be made available to the research community: the core concepts, the indirect co-occurrence matrices, and all available ratings. Given WordNet's past contributions to a number of diverse disciplines, the initial stages of the construction of this research tool should stimulate great interest and have a significant impact on related work.