SGER: Toward figurative language interpretation: A pilot study

该研究解决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的行为，自然语言理解系统需要丰富的语义知识和复杂的推理方法来处理比喻语言。比喻语言在日常话语中的作用的一个典型例子是普遍使用隐喻来执行大多数语言功能，包括陈述、修饰和指称。到目前为止，还没有一个全面的计算系统来处理隐喻的所有方面，甚至还没有设计出全面的计算系统。我们的项目试图通过解决在构建可扩展的、开放源代码的隐喻模型方面的关键挑战来解决这种情况。在之前建模工作的基础上，利用认知语言学的成果，我们正在探索a)设计和填充机器可读隐喻本体的技术，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源识别隐喻。在实际规模上，该项目提供了一个机器可读隐喻本体，允许编程访问数据的关系结构，增强重用，并促进增量开发。再加上隐喻识别工具，这使我们在一定程度上(伴随的需求是语料库注释工作)创建了一个开放的、可扩展的隐喻资源。在科学方面，该项目建立了对隐喻推理的形式和内容的更好理解，并创建了第一个计算机框架，以探索从文本、语言和媒体自动处理这一信息。

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.