SOTAVerified

Word Similarity

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

Papers

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Showing 226–250 of 378 papers

TitleStatusHype
Improving Distributional Similarity with Lessons Learned from Word Embeddings—0
Improving Multilingual Neural Machine Translation For Low-Resource Languages: French,English - Vietnamese—0
Improving Multilingual Neural Machine Translation For Low-Resource Languages: French, English - Vietnamese—0
Improving sparse word similarity models with asymmetric measures—0
Improving Word Alignment using Word Similarity—0
Improving Word Embeddings through Iterative Refinement of Word- and Character-level Models—0
Improving Word Embeddings Using Kernel PCA—0
Injecting Word Embeddings with Another Language's Resource : An Application of Bilingual Embeddings—0
Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction—0
Interpretable probabilistic embeddings: bridging the gap between topic models and neural networks—0
Investigating Cross-Lingual Alignment Methods for Contextualized Embeddings with Token-Level Evaluation—0
Joint Embeddings of Chinese Words, Characters, and Fine-grained Subcharacter Components—0
Joint Training for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora—0
Joint Unsupervised Learning of Semantic Representation of Words and Roles in Dependency Trees—0
JUSTMasters at SemEval-2020 Task 3: Multilingual Deep Learning Model to Predict the Effect of Context in Word Similarity—0
KNET: A General Framework for Learning Word Embedding using Morphological Knowledge—0
k-Rater Reliability: The Correct Unit of Reliability for Aggregated Human Annotations—0
L2F/INESC-ID at SemEval-2017 Tasks 1 and 2: Lexical and semantic features in word and textual similarity—0
Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction—0
Latent Semantic Analysis Models on Wikipedia and TASA—0
BURT: BERT-inspired Universal Representation from Twin Structure—0
Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision—0
Learning Contextual Embeddings for Structural Semantic Similarity using Categorical Information—0
Learning Geometric Word Meta-Embeddings—0
Learning Multi-Sense Word Distributions using Approximate Kullback-Leibler Divergence—0
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Benchmark Results

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