SGER: Toward figurative language interpretation: A pilot study

本研究探讨了自然语言理解中的隐喻推理问题。语言本质上是一种创造性行为，NLU系统需要广泛的语义知识和能够处理比喻语言的复杂推理方法。比喻语言在日常话语中的力量的一个典型例子是无处不在的使用隐喻来执行大多数语言功能，包括谓词、修饰和指称。到目前为止，还没有一个全面的计算系统能够处理隐喻的所有方面，甚至没有设计出来。我们的项目试图通过解决构建可伸缩的、开源的隐喻模型中的关键挑战来纠正这种情况。基于之前的建模工作并利用认知语言学的结果，我们正在探索以下技术：a)设计和填充机器可读的隐喻本体，b)分析关键话语信息的隐喻编码，包括事件结构和交际意图，以及c)使用机器学习算法从文本来源进行隐喻识别。在实际规模上，该项目提供了一个机器可读的隐喻本体，允许对数据的关系结构进行编程访问，增强重用，并促进增量开发。与隐喻识别工具相结合，这使我们朝着创建开放和可扩展的隐喻资源的方向前进了一部分（伴随的需求是语料库注释工作）。从科学的角度来看，该项目对隐喻推理的形式和内容有了更好的理解，并为探索文本语言和媒体对这些信息的自动处理提供了第一个计算框架。

This SGER addresses the problem of metaphoric inference in Natural Language Understanding (NLU). Language is fundamentally a creative act and NLU systems require extensive semantic knowledge and sophisticated inference methods that are capable of dealing with figurative language. A prototypical example of the power of figurative language in everyday discourse is the ubiquitous use of metaphor to perform most linguistic functions including predication, modification, and reference.To date, no comprehensive computational system addressing all the aspects of metaphor has been implemented or even designed. Our project seeks to remedy this situation by addressing key challenges in building a scalable, open source, model of metaphor. Building on previous modeling work and leveraging results from Cognitive Linguistics, we are exploring techniques to a) design and populate a machine readable metaphor ontology, b) analyze the metaphoric encoding of crucial discourse information, including event structure and communicative intent, and c) use machine learning algorithms for metaphor recognition from textual sources.On a practical scale, this project provides a machine readable ontology of metaphors, allowing programmatic access to the relational structure of the data, enhancing reuse, and fostering incremental development. Coupled with the metaphor recognition tools, this takes us part way (a companion requirement is a corpus annotation effort) toward creating an open and scalable metaphor resource. From the scientific side, this project creates a better understanding of the form and content of metaphoric inference and the first computational framework to explore the automatic processing of this information from text languages and media.