@inproceedings{serour-etal-2026-mcmasternlp,
title = "{M}c{M}aster{NLP} at {K}nowledge{G}raph{E}val-2026: Heterogeneous Expert Systems for {A}rabic Knowledge Graph Construction",
author = "Serour, Abdelaziz Essam Abdalla and
Mohammed, Suhaila and
Rajnikhanth, Sruthi and
Abuzannad, Saeed and
Hu, Daniel and
Alshomary, Milad and
Lahnala, Allison",
editor = "Abdul-Mageed, Muhammad and
Elsayed, Tamer and
Bouamor, Houda and
El-Beltagy, Samhaa and
Al-Khalifa, Hend and
Abdelali, Ahmed and
Elmadany, AbdelRahim and
El Mekki, Abdellah and
Darwish, Kareem and
Alkhereyf, Sakhar and
Luqman, Hamzah and
Khalifa, Salam and
Magdy, Samar and
Alyafeai, Zaid and
Hamed, Injy and
Zaghouani, Wajdi and
Zitouni, Imed",
booktitle = "Proceedings of The Fourth {A}rabic Natural Language Processing Conference: Shared Tasks",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.arabicnlp-sharedtasks.154/",
pages = "1227--1237",
ISBN = "979-8-89176-497-2",
abstract = "Automated knowledge graph (KG) construction involves two core components: nested named entity recognition (NER) and relation extraction (RE). In this work, we address cross-domain nested NER alongside RE. We integrate domain adaptation methods within a mixture-of-experts ensemble system for nested NER from Arabic text, evaluated in a cross-domain setting. The system combines multiple Arabic pretrained encoders for token- and span-level classification with domain data augmentation, and integrates their predictions using a vote-based ensemble routing with a complementary XLM-R expert. For RE, we employ an adversarially trained relation classifier backed by auxiliary entity types, NLI reranking, and structural schema rules, treating {no{\_}relation} as a standard relation label. We additionally study a positive-relation setting that excludes {no{\_}relation} from both training and inference. Our NER system reaches 0.7438 micro F1 on the hidden test set. For RE, the positive-relation setting achieves 0.9608 micro F1, while the {no{\_}relation}-aware system achieves 0.9295."
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<abstract>Automated knowledge graph (KG) construction involves two core components: nested named entity recognition (NER) and relation extraction (RE). In this work, we address cross-domain nested NER alongside RE. We integrate domain adaptation methods within a mixture-of-experts ensemble system for nested NER from Arabic text, evaluated in a cross-domain setting. The system combines multiple Arabic pretrained encoders for token- and span-level classification with domain data augmentation, and integrates their predictions using a vote-based ensemble routing with a complementary XLM-R expert. For RE, we employ an adversarially trained relation classifier backed by auxiliary entity types, NLI reranking, and structural schema rules, treating no_relation as a standard relation label. We additionally study a positive-relation setting that excludes no_relation from both training and inference. Our NER system reaches 0.7438 micro F1 on the hidden test set. For RE, the positive-relation setting achieves 0.9608 micro F1, while the no_relation-aware system achieves 0.9295.</abstract>
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%0 Conference Proceedings
%T McMasterNLP at KnowledgeGraphEval-2026: Heterogeneous Expert Systems for Arabic Knowledge Graph Construction
%A Serour, Abdelaziz Essam Abdalla
%A Mohammed, Suhaila
%A Rajnikhanth, Sruthi
%A Abuzannad, Saeed
%A Hu, Daniel
%A Alshomary, Milad
%A Lahnala, Allison
%Y Abdul-Mageed, Muhammad
%Y Elsayed, Tamer
%Y Bouamor, Houda
%Y El-Beltagy, Samhaa
%Y Al-Khalifa, Hend
%Y Abdelali, Ahmed
%Y Elmadany, AbdelRahim
%Y El Mekki, Abdellah
%Y Darwish, Kareem
%Y Alkhereyf, Sakhar
%Y Luqman, Hamzah
%Y Khalifa, Salam
%Y Magdy, Samar
%Y Alyafeai, Zaid
%Y Hamed, Injy
%Y Zaghouani, Wajdi
%Y Zitouni, Imed
%S Proceedings of The Fourth Arabic Natural Language Processing Conference: Shared Tasks
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-497-2
%F serour-etal-2026-mcmasternlp
%X Automated knowledge graph (KG) construction involves two core components: nested named entity recognition (NER) and relation extraction (RE). In this work, we address cross-domain nested NER alongside RE. We integrate domain adaptation methods within a mixture-of-experts ensemble system for nested NER from Arabic text, evaluated in a cross-domain setting. The system combines multiple Arabic pretrained encoders for token- and span-level classification with domain data augmentation, and integrates their predictions using a vote-based ensemble routing with a complementary XLM-R expert. For RE, we employ an adversarially trained relation classifier backed by auxiliary entity types, NLI reranking, and structural schema rules, treating no_relation as a standard relation label. We additionally study a positive-relation setting that excludes no_relation from both training and inference. Our NER system reaches 0.7438 micro F1 on the hidden test set. For RE, the positive-relation setting achieves 0.9608 micro F1, while the no_relation-aware system achieves 0.9295.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.154/
%P 1227-1237
Markdown (Informal)
[McMasterNLP at KnowledgeGraphEval-2026: Heterogeneous Expert Systems for Arabic Knowledge Graph Construction](https://aclanthology.org/2026.arabicnlp-sharedtasks.154/) (Serour et al., ArabicNLP 2026)
ACL