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        <title>Solving Hard Analogy Questions with Relation Embedding Chains</title>
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            <namePart type="given">Houda</namePart>
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            <namePart type="given">Kalika</namePart>
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    <abstract>Modelling how concepts are related is a central topic in Lexical Semantics. A common strategy is to rely on knowledge graphs (KGs) such as ConceptNet, and to model the relation between two concepts as a set of paths. However, KGs are limited to a fixed set of relation types, and they are incomplete and often noisy. Another strategy is to distill relation embeddings from a fine-tuned language model. However, this is less suitable for words that are only indirectly related and it does not readily allow us to incorporate structured domain knowledge. In this paper, we aim to combine the best of both worlds. We model relations as paths but associate their edges with relation embeddings. The paths are obtained by first identifying suitable intermediate words and then selecting those words for which informative relation embeddings can be obtained. We empirically show that our proposed representations are useful for solving hard analogy questions.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.382</identifier>
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%0 Conference Proceedings
%T Solving Hard Analogy Questions with Relation Embedding Chains
%A Kumar, Nitesh
%A Schockaert, Steven
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F kumar-schockaert-2023-solving
%X Modelling how concepts are related is a central topic in Lexical Semantics. A common strategy is to rely on knowledge graphs (KGs) such as ConceptNet, and to model the relation between two concepts as a set of paths. However, KGs are limited to a fixed set of relation types, and they are incomplete and often noisy. Another strategy is to distill relation embeddings from a fine-tuned language model. However, this is less suitable for words that are only indirectly related and it does not readily allow us to incorporate structured domain knowledge. In this paper, we aim to combine the best of both worlds. We model relations as paths but associate their edges with relation embeddings. The paths are obtained by first identifying suitable intermediate words and then selecting those words for which informative relation embeddings can be obtained. We empirically show that our proposed representations are useful for solving hard analogy questions.
%R 10.18653/v1/2023.emnlp-main.382
%U https://aclanthology.org/2023.emnlp-main.382
%U https://doi.org/10.18653/v1/2023.emnlp-main.382
%P 6224-6236

Markdown (Informal)

[Solving Hard Analogy Questions with Relation Embedding Chains](https://aclanthology.org/2023.emnlp-main.382) (Kumar & Schockaert, EMNLP 2023)

• Solving Hard Analogy Questions with Relation Embedding Chains (Kumar & Schockaert, EMNLP 2023)

ACL

• Nitesh Kumar and Steven Schockaert. 2023. Solving Hard Analogy Questions with Relation Embedding Chains. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6224–6236, Singapore. Association for Computational Linguistics.