A Lexical Knowledge Base from a Machine Readable Dictionary

机器可读词典（MRD）包含以下知识： 语言和自然界中的大规模任务所必需的世界 语言处理（NLP）。 然而，这些知识， 由词典编纂者为人类读者记录， 以足够原则的方式提出，以便使用MRD 直接作为NLP任务的工具。 NLP研究社区 需要的是机器可处理的字典（MTD）;也就是MRD 转换成NLP任务的格式。 先前的工作已经 开发了一系列从MRD中提取信息的工具， 一般来说，NLP任务。 这项工作将联合收割机结合这些方法 根据各自的优势。 它将定义螺旋 结合了三种方法的各种要素的方法 一个连贯的过程。 不同形式的知识 是从每一个单独的方法，每一个是不完整的， 在某些方面;然而，三者的结合会产生更好的效果。 结果比任何一种方法都好。 特别是 目标是构建一个MTD作为词汇事实的数据库， 以及一个语义相关的词义网络。 的 MTD将支持自动构建词典， 类型的NLP系统，形成那些使用传统的方法， 从语法和语义学到完全基于统计学 方法.

Machine-readable dictionaries (MRD's) contain knowledge about language and the world essential for large-scale tasks in natural language processing (NLP). This knowledge, however, collected and recorded by lexicographers for human readers, in not presented in a principled enough manner for MRD's to be used directly as tools for NLP tasks. What the NLP research community needs is machine tractable dictionaries (MTD's); that is MRD's transformed into a format for NLP tasks. Previous work has developed a range of tools for extracting information from MRD's for NLP tasks in general. This work will combine these methods based on their respective strengths. It will define the SPIRAL methodology which combines various elements of the three methods into a single coherent procedure. Different forms of knowledge are derived from each individual method and each is incomplete in certain respects; yet the combination of the three yields better results than could any individual method. In particular, the goal is to construct an MTD as a data-base of lexical facts, together with a network of statistically-related word sense. The MTD will support the automatic construction of lexicons for many types of NLP systems, form those using traditional methods based on syntax and semantics to those based wholly on statistical methods.