发表机构
School of Engineering; Pablo de Olavide University（工程学院; 巴勃罗·德·奥拉维德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ITL是一种可解释的文档对齐方法，通过提取术语概貌并计算文本单元与结构化参考框架的概念亲和度，实现对文档与SDG等框架的有效对齐量化，结果可追溯至术语证据。
AI 中文摘要
测量文档与结构化参考框架之间的对齐度，需要识别分布在文本中的概念证据，并通过可量化、可解释、可追溯的指标进行报告。许多常用的检索和分类方法仅返回成对相似度得分或一个或多个类别标签，而能提供可直接追溯至支撑证据的概念级得分的方法较少。本文提出了Intelligent Target Locator（ITL），这是一种领域无关、语言可移植的方法，用于估计目标文档的文本单元与Structured Reference Document（SRD）中定义的概念之间的亲和度。ITL从SRD中归纳出由独立术语、二元组、三元组和共现关系构建的特定概念术语概貌，为每个术语分配结合概念隶属度、术语类型特异性和跨概念区分度的重要性权重，输出可在不同粒度级别聚合的文本单元-概念亲和度矩阵。我们使用17个可持续发展目标（SDGs）进行内部一致性评估，将每个官方目标陈述与从同一套描述符归纳的SRD进行对比，结果显示每个陈述都与对应概念达到最高亲和度，其余概念的平均亲和度相对于参考平均亲和度处于边缘水平，这种分离表明ITL能够区分框架的概念概貌。因此，ITL为量化文档与结构化框架的对齐度提供了通用基础，同时保持每个结果可追溯至支撑它的术语证据。
英文摘要
Measuring alignment between documents and structured reference frameworks requires identifying conceptual evidence distributed throughout the text and reporting it through measures that are quantitative, interpretable, and traceable. Many commonly used retrieval and classification approaches return either pairwise similarity scores or one or more class labels, whereas fewer methods provide concept-level scores that are directly traceable to the terminological evidence supporting them. We present \emph{Intelligent Target Locator} (ITL), a domain-agnostic and language-portable methodology that estimates the affinity between the textual units of a target document and the concepts defined in a \emph{Structured Reference Document} ($SRD$). From the $SRD$, ITL induces concept-specific terminological profiles built from independent terms, bigrams, trigrams, and co-occurrences. Each term is assigned an importance weight that combines concept membership, term-type specificity and inter-concept discriminability. The output is a textual-unit--concept affinity matrix that can be aggregated at different levels of granularity. We conduct an internal consistency assessment using the 17 Sustainable Development Goals (SDGs), evaluating each official goal statement against the $SRD$ induced from the same set of descriptors. Every statement reached its highest affinity with the corresponding concept, and the mean affinity across the remaining concepts stayed marginal relative to the mean reference affinity. This separation indicates that ITL distinguishes the conceptual profiles of the framework. ITL thus offers a general basis for quantifying document alignment with structured frameworks while keeping each result traceable to the terminological evidence that supports it.
Comments21 pages, 2 figures, 7 tables, 2 Appendices
