CRII:RI:A Multi-level Framework for Text Specificity

语言特异性，它捕捉文本中的细节水平，在整个话语中变化，反映说话者的意图：一般内容提供高层次的观察或主张，而具体内容提供具体的细节。恰当地衡量具体性是组织良好的文本的一个特征，在理解中起着至关重要的作用。预测句子特异性的智能系统对自然语言处理任务（如摘要和论证挖掘）以及法医学、政治学和教育等领域的语言分析都很有用。然而，现有的系统是仅在句子级别上操作的特定于域的工具。该项目解决了一系列挑战，以实现领域不可知的，多层次的特异性分析，汇集跨计算方法，语言分析和语料库开发的概念。它开辟了在广泛的学科中应用特异性作为实用工具的场所，包括那些使用计算方法正在迅速出现的学科。为了大大提高我们对文本特异性的理解和实际使用，以捕获话语中的信息组织，这个CISE研究启动计划（CRII）项目旨在（1）开发跨多个领域工作的有效特异性预测系统，（2）开发注释指南和数据集，以细微差别的方式捕获特异性，（3）开发从多个层次的语言分析（单词、短语和句子）预测特异性的技术。在这些任务中，该项目绘制文本的具体变化，其语用功能，以及它们对话语结构的影响之间的联系。该项目还开发了将文本特异性集成到自然语言生成任务中的新方法，例如对话生成系统。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Language specificity, which captures the level of detail in text, varies throughout discourse as a reflection of speaker intent: general content makes high-level observations or claims, while specific content provides concrete details. Appropriately gauged specificity is a characteristic of well-organized text and plays a crucial role in comprehension. Intelligent systems that make predictions of sentence specificity have been useful for natural language processing tasks such as summarization and argumentation mining, as well as for linguistic analyses in fields such as forensic science, political science, and education. However, existing systems are domain-specific tools that operate only on the sentence level. This project addresses a series of challenges to enable domain-agnostic, multi-level analysis of specificity, bringing together concepts across computational methods, linguistic analysis and corpora development. It opens up venues of applying specificity as a pragmatic tool in a wide range of disciplines, including those where the use of computational methods is rapidly emerging. To substantially advance our understanding and practical use of text specificity to capture information organization in discourse, this CISE Research Initiation Initiative (CRII) project aims to (1) develop effective specificity prediction systems that work across multiple domains, (2) develop annotation guidelines and datasets to capture specificity in a nuanced way, (3) develop techniques to predict specificity from multiple levels of linguistic analysis (words, phrases and sentences). Throughout these tasks, the project draws connections between specificity variations in text, their pragmatic functions, and their impact on discourse structure. This project also develops new methods that integrate text specificity in natural language generation tasks, for example, dialog generation systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.