Constructing an Enhanced Version of WordNet

构建 WordNet 的增强版本

• 批准号: 0414072
• 负责人: Christiane Fellbaum
• 金额: $ 10万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0414072&HistoricalAwards=false
• 关键词: Constructing; Enhanced; Version; WordNet

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.

WordNet是NLP和AI等领域研究的重要词汇资源。 该项目启动了WordNet的一个根本增强版本的开发。 构建WordNet+涉及经验方法的新颖组合：人工注释，语料库分析和机器学习。 WordNet+专门解决了WordNet识别词义的有限能力，这是由于同义词集合（“synset”）之间布尔弧的稀疏性。 首先，要在5，000个同义词的核心集合中添加量化的、定向的弧。 这些弧线反映了唤起--一个同义词的意义在多大程度上让人想起另一个同义词。 在选择核心同义词集之后，将从注释器中引出250，000个弧的随机子集。 注释者经过训练和内部和内部可靠性测试，使用专门设计和测试的界面记录他们的心理关联的强度。 其余的弧将使用机器学习算法从手动获得的弧中推断出来。所有结果都将提供给研究界：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，这一研究工具的建设的初始阶段应该会激发人们的极大兴趣，并对相关工作产生重大影响。