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

本研究的主要目标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，为什么要写东西，以及它将如何影响读者。这与以前绝大多数关于语义处理的研究形成鲜明对比，其中主要关注的是理解语言的指称意义，即，什么是写在文本。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，如自动检测和修改的偏见，在现代文学和媒体，可以对少数民族和代表性不足的群体。这项研究将发展内涵框架作为一种新的表征形式主义，组织各种内涵的含义与特定的一个predicate的选择这种表示将大大扩展现有的资源框架语义，主要集中在指称意义，通过引入新的类型化关系编码的各个方面的内涵意义。利用单词和短语的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来产生内涵意义。所学习的表示将被组织为ContotationNet，这是一个不断发展的词、框架和短语的广泛覆盖的内涵词典。然后，将在此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存储在ContotationNet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，可以学习修改或撰写文本所需的内涵效果，特别关注现代文学和媒体对代表性不足的群体的不必要的偏见。

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.