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Register a new userSynonymy = Translational Equivalence
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Added by
Bradley Hauer
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Synonymy and translational equivalence are the relations of sameness of meaning within and across languages. As the principal relations in wordnets and multi-wordnets, they are vital to computational lexical semantics, yet the field suffers from the absence of a common formal framework to define their properties and relationship. This paper proposes a unifying treatment of these two relations, which is validated by experiments on existing resources. In our view, synonymy and translational equivalence are simply different types of semantic identity. The theory establishes a solid foundation for critically re-evaluating prior work in cross-lingual semantics, and facilitating the creation, verification, and amelioration of lexical resources.
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In a recent issue of Linguistics and Philosophy Kasmi and Pelletier (1998) (K&P), and Westerstahl (1998) criticize Zadroznys (1994) argument that any semantics can be represented compositionally. The argument is based upon Zadroznys theorem that every meaning function m can be encoded by a function mu such that (i) for any expression E of a specified language L, m(E) can be recovered from mu(E), and (ii) mu is a homomorphism from the syntactic structures of L to interpretations of L. In both cases, the primary motivation for the objections brought against Zadroznys argument is the view that his encoding of the original meaning function does not properly reflect the synonymy relations posited for the language. In this paper, we argue that these technical criticisms do not go through. In particular, we prove that mu properly encodes synonymy relations, i.e. if two expressions are synonymous, then their compositional meanings are identical. This corrects some misconceptions about the function mu, e.g. Janssen (1997). We suggest that the reason that semanticists have been anxious to preserve compositionality as a significant constraint on semantic theory is that it has been mistakenly regarded as a condition that must be satisfied by any theory that sustains a systematic connection between the meaning of an expression and the meanings of its parts. Recent developments in formal and computational semantics show that systematic theories of meanings need not be compositional.
Conventional Knowledge Graph Completion (KGC) assumes that all test entities appear during training. However, in real-world scenarios, Knowledge Graphs (KG) evolve fast with out-of-knowledge-graph (OOKG) entities added frequently, and we need to represent these entities efficiently. Most existing Knowledge Graph Embedding (KGE) methods cannot represent OOKG entities without costly retraining on the whole KG. To enhance efficiency, we propose a simple and effective method that inductively represents OOKG entities by their optimal estimation under translational assumptions. Given pretrained embeddings of the in-knowledge-graph (IKG) entities, our method needs no additional learning. Experimental results show that our method outperforms the state-of-the-art methods with higher efficiency on two KGC tasks with OOKG entities.
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