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Word Similarity

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–150 of 378 papers

TitleStatusHype
Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product ManifoldCode0
Word Sense Disambiguation Based on Word Similarity Calculation Using Word Vector Representation from a Knowledge-based GraphCode0
Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector SpecializationCode0
Incorporating Latent Meanings of Morphological Compositions to Enhance Word EmbeddingsCode0
Improved Biomedical Word Embeddings in the Transformer EraCode0
How to evaluate word embeddings? On importance of data efficiency and simple supervised tasksCode0
Improved Word Representation Learning with SememesCode0
Integrating Semantic Knowledge into Lexical Embeddings Based on Information Content MeasurementCode0
Matrix Factorization using Window Sampling and Negative Sampling for Improved Word RepresentationsCode0
BCWS: Bilingual Contextual Word SimilarityCode0
Semantic Word Clusters Using Signed Normalized Graph CutsCode0
Improving Reliability of Word Similarity Evaluation by Redesigning Annotation Task and Performance MeasureCode0
COS960: A Chinese Word Similarity Dataset of 960 Word PairsCode0
Word2Bits - Quantized Word VectorsCode0
Can Eye Movement Data Be Used As Ground Truth For Word Embeddings Evaluation?—0
Evaluation of word embeddings against cognitive processes: primed reaction times in lexical decision and naming tasks—0
Angular-Based Word Meta-Embedding Learning—0
Evaluation of taxonomic and neural embedding methods for calculating semantic similarity—0
Evaluation of Morphological Embeddings for English and Russian Languages—0
Evaluating Ways of Adapting Word Similarity—0
Aff2Vec: Affect--Enriched Distributional Word Representations—0
Embracing Error to Enable Rapid Crowdsourcing—0
Bridging Natural Language Processing and Psycholinguistics: computationally grounded semantic similarity datasets for Basque and Spanish—0
EVALution-MAN: A Chinese Dataset for the Training and Evaluation of DSMs—0
Embedding Word Similarity with Neural Machine Translation—0
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