多语言时间与数值表达式提取和规范化的数据集与基线系统
Dataset and Baseline System for Multi-lingual Extraction and Normalization of Temporal and Numerical Expressions
AI总结：
本文针对时间与数值表达式理解中子类型覆盖少、仅重提取及语种局限的问题，提出了覆盖14种语言的多语言评估数据集NTX及基于规则的强基线系统，支持提取、规范化与解析任务。
摘要：
时间与数值表达式的理解在许多下游自然语言处理（NLP）和信息检索（IR）任务中至关重要。然而，以往大量研究仅覆盖少数子类型，且仅专注于实体提取，这严重限制了所识别提及的可用性。为使此类实体在下游场景中发挥作用，子类型的覆盖范围和粒度至关重要；更重要的是，需将其解析为可操作的具体值。此外，大多数先前的研究仅处理少数几种语言。在此，我们描述了一个多语言评估数据集——NTX，涵盖14种语言中多样的时间与数值表达式，并覆盖提取、规范化和解析任务。随数据集一同提供的还有一个稳健的基于规则的系统，作为其他模型在该数据集上进行评估比较的强基线。数据和代码可在 \url{https://aka.ms/NTX} 获取。
英文摘要：
Temporal and numerical expression understanding is of great importance in many downstream Natural Language Processing (NLP) and Information Retrieval (IR) tasks. However, much previous work covers only a few sub-types and focuses only on entity extraction, which severely limits the usability of identified mentions. In order for such entities to be useful in downstream scenarios, coverage and granularity of sub-types are important; and, even more so, providing resolution into concrete values that can be manipulated. Furthermore, most previous work addresses only a handful of languages. Here we describe a multi-lingual evaluation dataset - NTX - covering diverse temporal and numerical expressions across 14 languages and covering extraction, normalization, and resolution. Along with the dataset we provide a robust rule-based system as a strong baseline for comparisons against other models to be evaluated in this dataset. Data and code are available at \url{https://aka.ms/NTX}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
