Questions within surveys, called survey items, are used in the social sciences to study latent concepts, such as the factors influencing life satisfaction. Instead of using explicit citations, researchers paraphrase the content of the survey items they use in-text. However, this makes it challenging to find survey items of interest when comparing related work. Automatically parsing and linking these implicit mentions to survey items in a knowledge base can provide more fine-grained references. We model this task, called Survey Item Linking (SIL), in two stages: mention detection and entity disambiguation. Due to an imprecise definition of the task, existing datasets used for evaluating the performance for SIL are too small and of low-quality. We argue that latent concepts and survey item mentions should be differentiated. To this end, we create a high-quality and richly annotated dataset consisting of 20,454 English and German sentences. By benchmarking deep learning systems for each of the two stages independently and sequentially, we demonstrate that the task is feasible, but observe that errors propagate from the first stage, leading to a lower overall task performance. Moreover, mentions that require the context of multiple sentences are more challenging to identify for models in the first stage. Modeling the entire context of a document and combining the two stages into an end-to-end system could mitigate these problems in future work, and errors could additionally be reduced by collecting more diverse data and by improving the quality of the knowledge base. The data and code are available at https://github.com/e-tornike/SIL .

翻译：调查中的问题，即调查项目，在社会科学中被用于研究潜在概念，例如影响生活满意度的因素。研究人员在文中并非使用明确的引用，而是对所用调查项目的内容进行转述。然而，这在比较相关工作时使得寻找感兴趣的调查项目变得困难。自动解析这些隐含提及并将其链接到知识库中的调查项目，可以提供更细粒度的参考文献。我们将此任务（称为调查项目链接）建模为两个阶段：提及检测和实体消歧。由于任务定义不精确，现有用于评估调查项目链接性能的数据集规模过小且质量较低。我们认为，潜在概念和调查项目提及应加以区分。为此，我们创建了一个高质量、注释丰富的数据集，包含20,454个英语和德语句子。通过对两个阶段分别和顺序地进行深度学习系统基准测试，我们证明该任务是可行的，但观察到错误从第一阶段传播，导致整体任务性能降低。此外，需要多句子上下文才能理解的提及对于第一阶段的模型更具挑战性。在未来的工作中，对文档的整个上下文进行建模，并将两个阶段结合成一个端到端系统，可以缓解这些问题；同时，通过收集更多样化的数据和提高知识库的质量，还可以进一步减少错误。数据和代码可在 https://github.com/e-tornike/SIL 获取。