RI: Small: ConnotationNet: Modeling Non-Literal Meaning in Context

本研究的主要目标是开发一种新的计算框架，以恢复和推理语言中广泛的内涵意义，即为什么要写一些东西以及它将如何影响读者。这与之前绝大多数关于语义处理的研究形成对比，语义处理的主要重点是理解语言的指称意义，即文本中所写的内容。这项研究将为需要理解文本中的非字面意义的广泛任务创造新的计算解决方案，包括社会上重要的挑战，如自动检测和修正现代文学和媒体中的偏见，这些偏见可能会对少数民族和代表性不足的群体产生影响。本研究将发展内涵框架作为一种新的表征形式主义，以组织与特定谓词选择相关的各种内涵含义。通过引入新的类型关系对内涵意义的各个方面进行编码，这种表示将极大地扩展框架语义的现有资源，这些资源主要集中在指称意义上。利用单词和短语分布表示的最新进展，本研究将开发可以从大规模自然语言语料库中推断内涵框架的算法，这反映了人们如何在上下文中使用语言产生内涵意义。学习到的表征将被组织成一个内涵网，这是一个不断发展的涵盖广泛的词汇、框架和短语的内涵词典。在这个词典中编码的知识将用于文档级文本理解，其中文本中部分呈现的信息将与存储在ConnotationNet中的丰富内涵知识相结合，以推断给定文本的完整文档级内涵。与此同时，本研究将寻求新的语言生成模型，该模型可以学习修改或编写具有所需内涵效果的文本，并特别关注现代文学和媒体对代表性不足群体的不必要偏见。

The major goal of this research is to develop a new computational framework to recover and reason about a wide range of connotative meanings in language, i.e., why something is written and how it will affect the readers. This contrasts with the vast majority of previous research on semantic processing, where the primary focus has been on understanding the denotational meaning of language, i.e., what is written in text. This research will create new computational solutions to a wide range of tasks that require understanding non-literal meaning in text, including societally important challenges such as automatic detection and revision of biases in modern literature and media that can work against minorities and underrepresented groups.This research will develop Connotation Frames as a new representation formalism to organize a variety of connotative implications associated with a particular choice of a predicate. This representation will substantially extend the existing resources of frame semantics, which has focused primarily on denotational meanings, by introducing new typed relations to encode various aspects of connotative meanings. Capitalizing on recent advances in distributional representation of words and phrases, this research will develop algorithms that can infer connotation frames from a large-scale natural language corpus, which reflects how connotative meanings arise from how people use language in context. The learned representations will be organized as ConnotationNet, an evolving broad-coverage connotation lexicon for words, frames, and phrases. Knowledge encoded in this lexicon will then be used for document-level text understanding, where partially present information in text will be combined with the rich connotative knowledge stored in ConnotationNet to infer the complete the document-level connotation of given text. In parallel, this research will seek new language generation models that can learn to revise or compose text with the desired connotative effects with specific focus on unwanted biases in modern literature and media against underrepresented groups.