发表机构
Kagawa University; Nikkei Inc.（香川大学; 日本经济新闻社）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对日语新闻中省略时间表达式导致的检索与RAG时间错配问题，提出基于规则的jaROTE流水线，利用发布日期复原时间，性能媲美大语言模型且高效低成本。
AI 中文摘要
新闻文章常常包含省略的时间表达式，例如仅提及日期或月份，这些表达式必须参照发布日期进行解读。当此类文章在搜索和检索增强生成（RAG）系统中被索引或作为独立文本处理时，这些省略可能导致时间错配以及大语言模型的不稳定解读。我们专注于在文章被索引用于搜索和RAG应用之前，利用发布日期作为外部上下文，将省略的时间表达式复原为具体日期或区间。具体而言，基于已有的时间表达式提取与规范化技术，并结合对日语新闻文章的人工分析，我们提出了jaROTE，一个面向日语新闻的基于规则的流水线。在两个新闻语料库上的实验表明，jaROTE实现了高性能，并且在与大语言模型竞争的同时，提供了快速、低成本的流水线。我们进一步表明，时间复原改善了受时间约束的词汇检索，证明了基于发布日期的规范化对日语新闻检索的实际价值。
英文摘要
News articles often contain omitted temporal expressions, such as day-only or month-only mentions, which must be interpreted with reference to the publication date. When such articles are indexed or processed as standalone text in search and retrieval-augmented generation (RAG) systems, these omissions can cause temporal mismatches and unstable interpretation by large language models. We focus on reproducing omitted temporal expressions as concrete dates or intervals using the publication date as external context before the articles are indexed for search and RAG applications. Specifically, building on established temporal-expression extraction and normalization techniques and informed by a manual analysis of Japanese news articles, we propose jaROTE, a rule-based pipeline for Japanese news. Experiments on two news corpora demonstrate that jaROTE achieves high performance, and remains competitive with LLMs while providing a fast, low-cost pipeline. We further show that temporal reproduction improves time-constrained lexical retrieval, demonstrating the practical value of publication-date-grounded normalization for Japanese news retrieval.
CommentsEMNLP 2026 Industry Track
