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

这项研究的主要目的是开发一个新的计算框架来恢复和推理语言中广泛的内涵意义，即为什么要写某事以及它将如何影响读者。这与以往对语义加工的绝大多数研究不同，以往的研究主要集中在理解语言的外延意义，即文本中写的东西。这项研究将为一系列需要理解文本中非字面意义的任务创造新的计算解决方案，包括社会上重要的挑战，例如自动检测和修改现代文学和媒体中可能对少数群体和代表不足的群体不利的偏见。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。这种表示将通过引入新的类型关系来编码内涵意义的各个方面，从而极大地扩展框架语义学的现有资源，框架语义学主要集中在外延意义上。这项研究将利用单词和短语分布表示的最新进展，开发能够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来产生内涵意义。学习的表示将被组织为ConnotationNet，这是一个不断发展的、涵盖广泛的词、框架和短语的内涵词典。然后将该词典中编码的知识用于文档级文本理解，其中文本中的部分呈现信息将与ConnotationNet中存储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。同时，这项研究将寻求新的语言生成模式，能够学习修改或撰写具有所需内涵效果的文本，特别关注现代文学和媒体中对代表不足的群体的不受欢迎的偏见。

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.