Can cross-domain term extraction benefit from cross-lingual transfer and nested term labeling?

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hanh Thi Hong Tran, Matej Martinc, Andraz Repar, Nikola Ljubešić, Antoine Doucet, Senja Pollak
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Abstract

Automatic term extraction (ATE) is a natural language processing task that eases the effort of manually identifying terms from domain-specific corpora by providing a list of candidate terms. In this paper, we treat ATE as a sequence-labeling task and explore the efficacy of XLMR in evaluating cross-lingual and multilingual learning against monolingual learning in the cross-domain ATE context. Additionally, we introduce NOBI, a novel annotation mechanism enabling the labeling of single-word nested terms. Our experiments are conducted on the ACTER corpus, encompassing four domains and three languages (English, French, and Dutch), as well as the RSDO5 Slovenian corpus, encompassing four additional domains. Results indicate that cross-lingual and multilingual models outperform monolingual settings, showcasing improved F1-scores for all languages within the ACTER dataset. When incorporating an additional Slovenian corpus into the training set, the multilingual model exhibits superior performance compared to state-of-the-art approaches in specific scenarios. Moreover, the newly introduced NOBI labeling mechanism enhances the classifier’s capacity to extract short nested terms significantly, leading to substantial improvements in Recall for the ACTER dataset and consequentially boosting the overall F1-score performance.

Abstract Image

跨域术语提取能否受益于跨语言转移和嵌套术语标注?
自动术语提取(ATE)是一项自然语言处理任务,它通过提供候选术语列表,减轻了从特定领域语料库中手动识别术语的工作量。在本文中,我们将 ATE 视为序列标注任务,并探讨了 XLMR 在跨领域 ATE 中评估跨语言和多语言学习与单语言学习的效果。此外,我们还引入了 NOBI,这是一种新颖的标注机制,可对单词嵌套术语进行标注。我们在 ACTER 语料库(包含四个域和三种语言(英语、法语和荷兰语))以及 RSDO5 斯洛文尼亚语料库(包含另外四个域)上进行了实验。结果表明,跨语言和多语言模型优于单语言设置,ACTER 数据集中所有语言的 F1 分数都有所提高。在将斯洛文尼亚语语料纳入训练集时,多语言模型在特定场景中的表现优于最先进的方法。此外,新引入的 NOBI 标签机制显著增强了分类器提取嵌套短词的能力,从而大幅提高了 ACTER 数据集的召回率,并因此提升了整体 F1 分数性能。
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来源期刊
Machine Learning
Machine Learning 工程技术-计算机:人工智能
CiteScore
11.00
自引率
2.70%
发文量
162
审稿时长
3 months
期刊介绍: Machine Learning serves as a global platform dedicated to computational approaches in learning. The journal reports substantial findings on diverse learning methods applied to various problems, offering support through empirical studies, theoretical analysis, or connections to psychological phenomena. It demonstrates the application of learning methods to solve significant problems and aims to enhance the conduct of machine learning research with a focus on verifiable and replicable evidence in published papers.
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