CAREER: Word Meaning: Beyond Dictionary Senses

大多数单词都有不止一个意思。词义的标准计算模型是通过字典意义列表。然而，选择正确的字典意义对人类和机器来说都是一项非常困难的任务。这个CAREER项目遵循的假设，基于当前的人类概念表示模型，即词义通过相似性的分级概念比通过字典意义更好地描述。通过一个新的意义注释框架和计算通过向量空间模型的词义的假设进行测试。该模型使用向量表征的典型参数来计算一个单独的发生在其句法上下文中的单词组成的意义。对于评估，该项目侧重于从基于相似性的意义表征中，在浅层和深层框架中得出适当推论的能力。研究工作与教育工作相结合，教育工作侧重于支持本科生研究，强调实践数据探索和跨学科工作。词义的表征是词汇语义学和整个计算语言学的核心问题。这个CAREER项目将产生一个广泛适用的范例，描述单词的意义，而不求助于字典的意义。它的目的是提供一个认知上更充分的模型，并有利于语言技术的应用，特别是信息检索，这已经严重依赖于向量空间模型。

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.