The Recent Advances in Automatic Term Extraction: A survey

Tran, Hanh Thi Hong; Martinc, Matej; Caporusso, Jaya; Doucet, Antoine; Pollak, Senja

doi:10.48550/arxiv.2301.06767

Cited by 1 publication

(1 citation statement)

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“…After this last filtering step, we obtained a list of twenty-four papers. A last comment about recent works on ATE, at the time of the preparation of this manuscript, we found on arXiv an interesting survey on recent advances on ATE that we feel obliged to mention giving the synchronicity with this manuscript (Tran et al, 2023). Despite the focus of this survey being mostly on neural transformer-based models, we found it very interesting also from the point of view of the analysis of the datasets and metrics used for the evaluation of the performance in ATE.…”

Section: Methodsmentioning

confidence: 86%

A systematic review of Automatic Term Extraction: What happened in 2022?

Nunzio

Marchesin

Silvello

2023

Digital Scholarship in the Humanities

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Automatic Term Extraction (ATE) systems have been studied for many decades as, among other things, one of the most important tools for tasks such as information retrieval, sentiment analysis, named entity recognition, and others. The interest in this topic has even increased in recent years given the support and improvement of the new neural approaches. In this article, we present a follow-up on the discussions about the pipeline that allows extracting key terms from medical reports, presented at MDTT 2022, and analyze the very last papers about ATE in a systematic review fashion. We analyzed the journal and conference papers published in 2022 (and partially in 2023) about ATE and cluster them into subtopics according to the focus of the papers for a better presentation.

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Section: Methodsmentioning

confidence: 86%