CAREER: Word Meaning: Beyond Dictionary Senses

职业：词义：超越字典意义

• 批准号: 0845925
• 负责人: Katrin Erk
• 金额: $ 43.35万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845925&HistoricalAwards=false
• 关键词: CAREER; Word; Meaning; Beyond; Dictionary

Most words have more than one meaning. The standard computational model for word meaning is through lists of dictionary senses. However, choosing the right dictionary sense is a highly difficult task for humans as well as machines. This CAREER project follows the hypothesis, based on current models of human concept representation, that word meaning is better described through a graded notion of similarity than through dictionary senses. The hypothesis is tested through a novel meaning annotation framework and computationally through a vector space model of word meaning. The model uses vector characterizations of typical arguments to compute the meaning of an individual occurrence compositionally from the words in its syntactic context. For evaluation, the project focuses on the ability to draw appropriate inferences, in both shallow and deep frameworks, from similarity-based meaning representations. The research effort goes together with educational work that focuses on supporting undergraduate research, stressing hands-on data exploration and interdisciplinary work.The characterization of word meaning is a central issue in lexical semantics and in computational linguistics as a whole. This CAREER project will yield a broadly applicable paradigm that describes word meaning without recourse to dictionary senses. It aims both to provide a more cognitively adequate model and to benefit language technology applications, in particular information retrieval, which already relies heavily on vector space models.

大多数单词具有多个含义。单词含义的标准计算模型是通过字典感官列表。但是，选择正确的词典意义对于人类和机器来说是一项艰巨的任务。这个职业项目遵循了基于人类概念表示模型的假设，该假设通过相似性的分级概念更好地描述了单词含义，而不是通过字典感官来描述。通过新颖的含义注释框架和计算通过单词含义的向量空间模型来检验该假设。该模型使用典型参数的矢量特征来计算单个出现在其句法上下文中的单词的含义。为了进行评估，该项目着重于从基于相似性的含义表示中汲取适当推论的能力。研究工作与侧重于支持本科研究的教育工作，强调动手数据探索和跨学科工作。单词含义的特征是词汇语义和整个计算语言学中的核心问题。这个职业项目将产生一个广泛适用的范式，该范式描述了词，而无需求助于字典感官。它旨在提供一个更具认知的模型，并使语言技术应用程序（特别是信息检索）受益，这已经很大程度上依赖于矢量空间模型。