SOTAVerified

Word Similarity

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

Papers

Showing 276–300 of 378 papers

TitleStatusHype
Learning Rare Word Representations using Semantic Bridging—0
Improve Lexicon-based Word Embeddings By Word Sense Disambiguation—0
Reconstruction of Word Embeddings from Sub-Word Parameters—0
A Critique of a Critique of Word Similarity Datasets: Sanity Check or Unnecessary Confusion?—0
A Simple Approach to Learn Polysemous Word EmbeddingsCode0
Improved Word Representation Learning with SememesCode0
Semantic Word Clusters Using Signed Spectral Clustering—0
Are distributional representations ready for the real world? Evaluating word vectors for grounded perceptual meaningCode0
Neural Embeddings of Graphs in Hyperbolic Space—0
Multimodal Word DistributionsCode1
ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational KnowledgeCode2
Word Similarity Datasets for Indian Languages: Annotation and Baseline Systems—0
Reranking Translation Candidates Produced by Several Bilingual Word Similarity Sources—0
Predicting Emotional Word Ratings using Distributional Representations and Signed Clustering—0
Cross-Lingual Syntactically Informed Distributed Word Representations—0
Integrating Semantic Knowledge into Lexical Embeddings Based on Information Content MeasurementCode0
Construction of a Japanese Word Similarity DatasetCode0
How to evaluate word embeddings? On importance of data efficiency and simple supervised tasksCode0
All-but-the-Top: Simple and Effective Postprocessing for Word RepresentationsCode0
Implicitly Incorporating Morphological Information into Word Embedding—0
Real Multi-Sense or Pseudo Multi-Sense: An Approach to Improve Word Representation—0
Improved Word Embeddings with Implicit Structure Information—0
Sub-Word Similarity based Search for Embeddings: Inducing Rare-Word Embeddings for Word Similarity Tasks and Language Modelling—0
Modifications of Machine Translation Evaluation Metrics by Using Word Embeddings—0
Definition Modeling: Learning to define word embeddings in natural languageCode0
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Benchmark Results

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