arXiv cs.CL· Arne Rubehn·· 12 小时前AI 评分18
基于跨语言共词化模式与文本属性的概念嵌入归纳学习框架
Inductive Learning of Concept Embeddings from Cross-Linguistic Colexification Patterns with Textual Attributes
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研究提出一种归纳式概念嵌入学习框架，将完整与部分共词化网络的结构属性与简短概念描述的语义信息结合，可为训练数据中未出现的新概念生成嵌入向量。此前基于跨语言共词化网络的概念嵌入依赖直推式方法、缺乏可扩展性，新框架支持仅凭简短定义嵌入未见概念。实验显示所得概念嵌入与多种跨语言相似度或关联度指标相关，且对新概念泛化良好。
正文 · 原文
Abstract:The advent of distributional semantics as a framework for computational semantics marked a paradigm shift in the nature of representations, by which meanings are represented as dense vectors commonly known as word embeddings. Computational approaches to historical or typological language comparison, on the other hand, still mostly rely on discrete representations, while the potential of embedding-based methods remains largely untapped in this context. Recent studies have proposed concept embeddings as a language-independent, onomasiological counterpart to word embeddings, based on networks of cross-linguistic colexification patterns. While the resulting concept embeddings reflect meaningful semantic properties and relations, they suffer from a lack of scalability, since they rely on transductive embedding approaches which only learn embeddings for nodes present in the training data. In this study, we present an inductive framework for learning concept embeddings, combining the structural properties of full and partial colexification networks with semantic information from short concept descriptions. This framework allows the embedding of unseen concepts based on a short definition. We show that the resulting concept embeddings correlate with various cross-linguistic measures of similarity or relatedness, while generalizing well to new concepts that were absent from the training data.
| Subjects: | Computation and Language (cs.CL) |
| ACM classes: | J.5 |
| Cite as: | arXiv:2610.22638 [cs.CL] |
| (or arXiv:2610.22638v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.22638 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Arne Rubehn [view email]
[v1]
Thu, 15 Oct 2026 20:53:31 UTC (253 KB)
来源：arXiv cs.CL · arxiv.org