Time expression recognition and normalization: a survey

Zhong, Xiaoshi; Wang, Zhaoxia

doi:10.1007/s10462-023-10400-y

Cited by 5 publications

(2 citation statements)

References 101 publications

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“…Zhong and Cambria (2023), [53], conducted a comprehensive review of the progress in Time Expression Recognition and Normalization (TERN), primarily focusing on three key approaches: rule-based, machine-learning, and deep learning. The authors also delve into the challenges faced by TERN and explore potential directions for future research.…”

Section: Related Workmentioning

confidence: 99%

Extraction of time-related expressions using text mining with application to Hebrew

Mughaz,

HaCohen-Kerner,

Gabbay

2024

PLoS ONE

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In this research, we extract time-related expressions from a rabbinic text in a semi-automatic manner. These expressions usually appear next to rabbinic references (name / nickname / acronym / book-name). The first step toward our goal is to find all the expressions near references in the corpus. However, not all of the phrases around the references are time-related expressions. Therefore, these phrases are initially considered to be potential time-related expressions. To extract the time-related expressions, we formulate two new statistical functions, and we use screening and heuristic methods. We tested these statistical functions, grammatical screenings, and heuristic methods on a corpus containing responsa documents. In this corpus, many rabbinic citations are known and marked. The statistical functions and the screening methods filtered the potential time-related expressions and reduced 99.88% of the initial expressions (from 484,681 to 575).

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Section: Related Workmentioning

confidence: 99%

Extraction of time-related expressions using text mining with application to Hebrew

Mughaz,

HaCohen-Kerner,

Gabbay

2024

PLoS ONE

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“…The task of temporal expression (TIMEX) extraction includes identifying each TIMEX in text, and marking the span in text for each identified expression. Zhong and Cambria proposed a survey [6] on time expression recognition and normalization task, which includes TIMEX extraction subtask.…”

Section: Temporal Information Extractionmentioning

confidence: 99%

Empirical study on temporal expression extraction via large language model

2024

Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024)

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Temporal expression extraction is to identify temporal expressions from raw text. Predominant methods rely on handcrafted rules or high-quality training datasets, and currently few works utilize large language models in this task. This paper demonstrated the ability of large language models to understand temporal information by building few-shot temporal expression extraction system that does not require hand-crafted rules or training. To reduce model output length, the system used phrase-only output schema instead of outputting entire sequence. The model also alleviated hallucination effect by prompting the model to predict expression type and using the model itself to filter model outputs. Experimental results indicated that the model achieved decent performance without training or rules.

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