@inproceedings{fujii-etal-2026-graph,
title = "Graph Neural Networks for Automatic Term Generation: Defining and Exploring Terminological Graph",
author = "Fujii, Toshihide and
Hazem, Amir and
Kageura, Kyo",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1507/",
pages = "32074--32091",
ISBN = "979-8-89176-462-0",
abstract = "Despite the advance in automatic term acquisition (ATA) research, the use of ATA in terminology work is limited. One of the main reasons is that most ATA research focuses on automatic term extraction (ATE) from textual corpora and does not consider terminologies. This paper introduces automatic term generation (ATG), a new method of ATA, which explores existing terminologies, in which timeline information is also considered. We propose a new way of representing the structure of a terminology as a graph, which consists of nodes that represent constituent elements of terms and two types of links that represent intra-term dependency between constituent elements and term closure between the first and the last constituent element of terms. ATG is then defined as a set of link prediction tasks, i.e. predicting two types of links. Cliques of the graph represent the new generated terms. They are determined in a backward fashion by first selecting nodes for which term closure links are predicted and then by walking backward to term nodes for which intra-term dependency links exist. Experiments showed that our approach gives high performance in predicting new terms."
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<abstract>Despite the advance in automatic term acquisition (ATA) research, the use of ATA in terminology work is limited. One of the main reasons is that most ATA research focuses on automatic term extraction (ATE) from textual corpora and does not consider terminologies. This paper introduces automatic term generation (ATG), a new method of ATA, which explores existing terminologies, in which timeline information is also considered. We propose a new way of representing the structure of a terminology as a graph, which consists of nodes that represent constituent elements of terms and two types of links that represent intra-term dependency between constituent elements and term closure between the first and the last constituent element of terms. ATG is then defined as a set of link prediction tasks, i.e. predicting two types of links. Cliques of the graph represent the new generated terms. They are determined in a backward fashion by first selecting nodes for which term closure links are predicted and then by walking backward to term nodes for which intra-term dependency links exist. Experiments showed that our approach gives high performance in predicting new terms.</abstract>
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%0 Conference Proceedings
%T Graph Neural Networks for Automatic Term Generation: Defining and Exploring Terminological Graph
%A Fujii, Toshihide
%A Hazem, Amir
%A Kageura, Kyo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F fujii-etal-2026-graph
%X Despite the advance in automatic term acquisition (ATA) research, the use of ATA in terminology work is limited. One of the main reasons is that most ATA research focuses on automatic term extraction (ATE) from textual corpora and does not consider terminologies. This paper introduces automatic term generation (ATG), a new method of ATA, which explores existing terminologies, in which timeline information is also considered. We propose a new way of representing the structure of a terminology as a graph, which consists of nodes that represent constituent elements of terms and two types of links that represent intra-term dependency between constituent elements and term closure between the first and the last constituent element of terms. ATG is then defined as a set of link prediction tasks, i.e. predicting two types of links. Cliques of the graph represent the new generated terms. They are determined in a backward fashion by first selecting nodes for which term closure links are predicted and then by walking backward to term nodes for which intra-term dependency links exist. Experiments showed that our approach gives high performance in predicting new terms.
%U https://aclanthology.org/2026.findings-emnlp.1507/
%P 32074-32091
Markdown (Informal)
[Graph Neural Networks for Automatic Term Generation: Defining and Exploring Terminological Graph](https://aclanthology.org/2026.findings-emnlp.1507/) (Fujii et al., Findings 2026)
ACL