SOTAVerified

Word Similarity

Calculate a numerical score for the semantic similarity between two words.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 126–150 of 378 papers

TitleStatusHype
Expansional Retrofitting for Word Vector Enrichment—0
Bio-inspired Structure Identification in Language Embeddings—0
Embeddings models for Buddhist Sanskrit—0
Embedding Learning Through Multilingual Concept Induction—0
Bilingual Embeddings with Random Walks over Multilingual Wordnets—0
Anchor-based Bilingual Word Embeddings for Low-Resource Languages—0
Adversarial Training of Word2Vec for Basket Completion—0
A Computational Study on Word Meanings and Their Distributed Representations via Polymodal Embedding—0
Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space—0
Effect of Post-processing on Contextualized Word Representations—0
EDS-MEMBED: Multi-sense embeddings based on enhanced distributional semantic structures via a graph walk over word senses—0
Balancing the composition of word embeddings across heterogenous data sets—0
An Artificial Language Evaluation of Distributional Semantic Models—0
DRCoVe: An Augmented Word Representation Approach using Distributional and Relational Context—0
Do We Need Neural Models to Explain Human Judgments of Acceptability?—0
Back to Basics for Monolingual Alignment: Exploiting Word Similarity and Contextual Evidence—0
Do Supervised Distributional Methods Really Learn Lexical Inference Relations?—0
Do Multi-Sense Embeddings Improve Natural Language Understanding?—0
BabelEnconding at SemEval-2020 Task 3: Contextual Similarity as a Combination of Multilingualism and Language Models—0
An Approach of Hybrid Hierarchical Structure for Word Similarity Computing by HowNet—0
Domain Mismatch Doesn’t Always Prevent Cross-lingual Transfer Learning—0
Domain Mismatch Doesn't Always Prevent Cross-Lingual Transfer Learning—0
Massively Multilingual Lexical Specialization of Multilingual Transformers—0
Distributed representation of multi-sense words: A loss-driven approach—0
Dirichlet-Smoothed Word Embeddings for Low-Resource Settings—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Context-to-VectorSpearman's Rho78.9—Unverified
2Bert2VecSpearman's Rho72.8—Unverified
3SkipGramSpearman's Rho61—Unverified