EAGER: Towards Inter-Sentential Models for Detecting Focus of Negation

EAGER：走向检测否定焦点的句间模型

• 批准号: 1734730
• 负责人: Eduardo Blanco
• 金额: $ 6万
• 依托单位: University of North Texas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734730&HistoricalAwards=false
• 关键词: EAGER; Towards; Inter; Sentential; Models

Understanding language is at the core of intelligent systems, and a required component of several end-user applications such as machine translation, question answering and text summarization. Most computational approaches to extract meaning from text build a semantic representation capturing some of the meaning intuitively understood by humans when reading text. For example, semantic role labelers extract who did what to whom, how, when and where from plain text, and semantic parsers transform text into logic forms. This exploratory EAGER project defines new algorithms for extracting positive interpretations from negated statements by detecting the focus of negation, that is, pinpointing the usually few text chunks within a negation that are intended to be negated.Negation is an intricate linguistic phenomenon present in all human languages. From a theoretical point of view, it is well known that negation often carries positive meanings ranging from implicatures to entailments. Despite this observation, current computational approaches to understand the effects of negation are limited to scope detection and disregard the numerous positive interpretations immediately understood by humans when reading text with negated elements. This project builds inter-sentential models to detect the foci of negation, a key component to extract positive interpretations from negations. Exploiting the context surrounding a negation is key to improving foci detection, as existing intra-sentential models fail to take into account key related events that signal the correct foci out of the many potential foci for a given negation. Being able to extract positive interpretations from negation will benefit the overall extraction of meaning from text, since once positive interpretations associated with negative expressions have been identified, they can be integrated with the interpretations obtained for the text as a whole.

理解语言是智能系统的核心，也是机器翻译、问答和文本摘要等几个最终用户应用程序的必需组件。大多数从文本中提取意义的计算方法都构建了一种语义表示，捕捉到了人类在阅读文本时直观理解的一些含义。例如，语义角色标签器从纯文本中提取谁对谁做了什么，如何，何时何地，语义解析器将文本转换为逻辑形式。这个探索性的热切项目定义了新的算法，通过检测否定的焦点来从否定的语句中提取积极的解释，即准确地找到否定中通常很少的文本块。否定是一种复杂的语言现象，存在于所有人类语言中。从理论的角度来看，众所周知，否定往往具有从含意到蕴涵的积极意义。尽管如此，目前理解否定的影响的计算方法仅限于范围检测，而忽略了人类在阅读带有否定成分的文本时立即理解的大量积极解释。这个项目建立了句子间模型来检测否定的焦点，这是从否定中提取积极解释的关键组成部分。利用否定词周围的语境是改进焦点检测的关键，因为现有的句内模型没有考虑到关键相关事件，这些事件发出信号通知给定否定词的许多潜在焦点中的正确焦点。能够从否定中提取积极的解释将有助于从文本中整体提取意义，因为一旦识别了与否定表达相关的积极解释，它们就可以与对整个文本的解释相结合。