Constructing an Enhanced Version of WordNet

构建 WordNet 的增强版本

基本信息

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

项目摘要

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。该项目启动了从根本增强的WordNet版本的开发。构建WordNet+涉及经验方法的新型组合：人类注释，语料库分析和机器学习。 WordNet+专门解决了WordNet识别单词感官的一些有限的能力，这是由于布尔弧的稀疏性在一组同义词中的稀疏性（“ Synsets”）。首先，应在5,000个合成集的核心组中添加量化的定向弧。这些弧形反映了唤起 - 一种同步的含义使另一个综合的含义在多大程度上。选择核心合成器后，将从注释器中引起250,000个弧的随机子集。接受培训和测试的注释者使用了可靠性和内部可靠性，并使用经过专门设计和测试的界面记录其心理关联的强度。其余的弧应从机器学习算法中从手动获得的弧进行外推。所有结果都将提供给研究社区：核心概念，间接的共发生矩阵以及所有可用的评级。鉴于WordNet过去对许多不同学科的贡献，该研究工具的构建的初始阶段应激发人们的兴趣，并对相关工作产生重大影响。