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This research addresses a critical bottleneck in Retrieval-Augmented Generation (RAG) systems by proposing jaROTE, a rule-based pipeline that resolves omitted temporal expressions in Japanese news using publication dates. By normalizing vague time references before indexing, the method ensures stable interpretation and improves time-constrained lexical retrieval while remaining computationally efficient compared to large language models. This work highlights the importance of preprocessing data with external context to mitigate hallucinations and errors inherent in standalone text processing for AI applications.
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Reproducing Omitted Temporal Expressions in Japanese News for Retrieval-Augmented Applications

arXiv2 min read

arXiv:2609.09569v1 Announce Type: new Abstract: 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.

Abstract: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.

Computation and Language (cs.CL)

arXiv:2609.09569 [cs.CL]

(or arXiv:2609.09569v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.09569 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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