EAGER: Building Idiomaticity into Natural Language Processing

习语是日常语言使用的重要组成部分，也是母语能力的标志。想想“扔掉”这个短语，熟练的演讲者可以毫不费力地理解这个短语在“英国扔掉了过去十年的所有成就”中的比喻意义。还有字面意义上的“他扔掉了香烟，把头埋在怀里。”EARLY探索性研究基金（EAGER）将为计算机建立一个高质量的数据集，以了解这些表达在一般英语文本中的比喻意义和字面意义之间的差异。这个项目的主要新奇在于收集了一大类习惯表达和包含它们的句子，让计算机学习各种习惯短语之间的内在差异。收集许多具有比喻和字面意义的短语的句子将使计算机更好地理解这些表达在日常对话和写作中使用的细微差别。除了了解他们，收集。示例将帮助计算机像母语使用者一样自动书写文本时使用这些表达，甚至在特定上下文中建议适当的表达。EAGER项目本质上是跨语言学和计算领域的跨学科项目，将研究具有习语意识的自然语言处理的新范式。因此，它将有两个研究目标：（1）创建一个高质量的短语动词数据集，并标注其上下文特定的含义及其字面/比喻等效形式，以及（2）测试最先进的习语感知算法的性能。由于习惯表达在形式和结构上有很大的不同，在探索性项目的背景下，对短语动词（也称为动词-小品词结构）的关注将允许研究一类非常常见的习惯表达，这些习惯表达在句法上与当前可用的数据集不同。该项目的主要风险来自于其创建大型语料库的探索性质，这些语料库具有足够的语言模型训练覆盖范围。鉴于它们在自然语言中的普遍性，英语短语动词数据集将补充现有的习语表达数据集的多样性。此外，它们在语境中的比喻和字面歧义（除了它们的一词多义之外）将允许对习语表达的非组合性现象进行不同的审视。因此，该数据集将作为检测、解释和生成广泛的习惯表达的算法的训练和测试平台。这一努力将导致新的自然语言处理算法，用于准确解释和生成习惯表达，以实现机器更像人类的语言处理能力。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Idiomatic expressions are an essential component of everyday language use and the hallmark of native language ability. Consider the phrase throw away; proficient speakers can effortlessly understand that the phrase takes a figurative meaning in “Britain threw away all the achievements of the last decade.” and a literal sense in “He threw away his cigarette and buried his head in his arms.” This EArly Grant for Exploratory Research (EAGER) will build a high-quality dataset for computers to understand the differences between figurative and literal senses of these expressions in general English text. The main novelty of this project will be in collecting a large class of idiomatic expressions and sentences containing them to let computers learn the inherent variability between a variety of idiomatic phrases. Collecting many sentences with phrases that have a figurative and literal meaning will permit computers better understand the nuances with which these expressions are used in everyday conversations and writing. Beyond understanding them, the collected. examples will help computers use these expressions like native speakers do when automatically writing text and even suggest appropriate expressions in specific contexts.This EAGER project is essentially interdisciplinary spanning the areas of linguistics and computation and will investigate novel paradigms for natural language processing that are idiomaticity-aware. As such, it will have two research aims: (1) creating a high-quality dataset of phrasal verbs annotated with their context-specific senses and their literal/figurative equivalent forms, and (2) testing the performance of state-of-the-art idiomaticity-aware algorithms. Because idiomatic expressions vary widely in form and structure, the focus on phrasal verbs (also known as verb-particle constructions) in the context of the exploratory project will permit studying a very frequent class of idiomatic expressions that are syntactically different from those in currently available datasets. The primary risk of this project stems from its exploratory nature of creating large corpora with sufficient coverage for language model training. Given their prevalence in natural language, the dataset of phrasal verbs in English will supplement available datasets on idiomatic expressions in terms of their variety. Moreover, their figurative and literal ambiguity in context (apart from their polysemy) will permit a diverse look at the phenomenon of non-compositionality that characterizes idiomatic expressions. Thus, the dataset will serve as a training and test bed for algorithms that detect, interpret, and generate a broad class of idiomatic expressions. This effort will lead to new natural language processing algorithms for accurate interpretation and generation of idiomatic expressions towards a more human-like language processing ability in machines.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.