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Retrofitting Word Vectors to Semantic Lexicons

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arxiv 1411.4166 v4 pith:EIBD5DNV submitted 2014-11-15 cs.CL

Manaal Faruqui , Jesse Dodge , Sujay K. Jauhar , Chris Dyer , Eduard Hovy , Noah A. Smith This is my paper · ORCID
classification cs.CL
keywords semanticvectorlexiconswordinformationrepresentationsmethodspace
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Vector space word representations are learned from distributional information of words in large corpora. Although such statistics are semantically informative, they disregard the valuable information that is contained in semantic lexicons such as WordNet, FrameNet, and the Paraphrase Database. This paper proposes a method for refining vector space representations using relational information from semantic lexicons by encouraging linked words to have similar vector representations, and it makes no assumptions about how the input vectors were constructed. Evaluated on a battery of standard lexical semantic evaluation tasks in several languages, we obtain substantial improvements starting with a variety of word vector models. Our refinement method outperforms prior techniques for incorporating semantic lexicons into the word vector training algorithms.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    Adapter-based tuning of mBERT and XLM-R improves low-resource language performance, with sequential bottlenecks best for language modeling and invertible bottlenecks best for downstream tasks, but pre-training data si...

  2. Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations

    cs.CL 2025-10 conditional novelty 4.0 of 10

    LLM explanations split into local and mechanistic tracks; the paper argues they are trustworthy only if they pass causal and contrastive stress tests, adapt to the explainee, and satisfy eight trust principles.

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