SOTAVerified

Word Similarity

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 378 papers

TitleStatusHype
Component-Enhanced Chinese Character Embeddings—0
Distributed representation of multi-sense words: A loss-driven approach—0
Do Multi-Sense Embeddings Improve Natural Language Understanding?—0
CitiusNLP at SemEval-2020 Task 3: Comparing Two Approaches for Word Vector Contextualization—0
A Retrofitting Model for Incorporating Semantic Relations into Word Embeddings—0
Confusion2Vec: Towards Enriching Vector Space Word Representations with Representational Ambiguities—0
Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings—0
Constructing a Word Similarity Graph from Vector based Word Representation for Named Entity Recognition—0
A Rule-based/BPSO Approach to Produce Low-dimensional Semantic Basis Vectors Set—0
A Self-Boosting Framework for Automated Radiographic Report Generation—0
Correlation-based Intrinsic Evaluation of Word Vector Representations—0
A Simple Approach to Learning Unsupervised Multilingual Embeddings—0
Creation and Evaluation of Datasets for Distributional Semantics Tasks in the Digital Humanities Domain—0
Cross-lingual Semantic Specialization via Lexical Relation Induction—0
Cross-Lingual Syntactically Informed Distributed Word Representations—0
Citius at SemEval-2017 Task 2: Cross-Lingual Similarity from Comparable Corpora and Dependency-Based Contexts—0
An Accurate Model for Predicting the (Graded) Effect of Context in Word Similarity Based on Bert—0
Deep Multilingual Correlation for Improved Word Embeddings—0
Attention Word Embedding—0
AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes—0
A Differentiable Self-disambiguated Sense Embedding Model via Scaled Gumbel Softmax—0
Automatic cognate identification with gap-weighted string subsequences.—0
Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings—0
Dirichlet-Smoothed Word Embeddings for Low-Resource Settings—0
A Proposal of Automatic Error Correction in Text—0
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Benchmark Results

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