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Word Similarity

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

Papers

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Showing 51–100 of 378 papers

TitleStatusHype
BabelEnconding at SemEval-2020 Task 3: Contextual Similarity as a Combination of Multilingualism and Language Models—0
Back to Basics for Monolingual Alignment: Exploiting Word Similarity and Contextual Evidence—0
Balancing the composition of word embeddings across heterogenous data sets—0
An Artificial Language Evaluation of Distributional Semantic Models—0
Bilingual Embeddings with Random Walks over Multilingual Wordnets—0
Bio-inspired Structure Identification in Language Embeddings—0
Are Word Embedding Methods Stable and Should We Care About It?—0
Bridging Natural Language Processing and Psycholinguistics: computationally grounded semantic similarity datasets for Basque and Spanish—0
Aff2Vec: Affect--Enriched Distributional Word Representations—0
A Computational Study on Word Meanings and Their Distributed Representations via Polymodal Embedding—0
Additive Compositionality of Word Vectors—0
Can Eye Movement Data Be Used As Ground Truth For Word Embeddings Evaluation?—0
A Comparison of the Word Similarity Measurement in English-Arabic Translation Memory Segment Retrieval Including an Inflectional Affix Intervention—0
EDS-MEMBED: Multi-sense embeddings based on enhanced distributional semantic structures via a graph walk over word senses—0
Embedding Word Similarity with Neural Machine Translation—0
Domain Mismatch Doesn't Always Prevent Cross-Lingual Transfer Learning—0
A critique of word similarity as a method for evaluating distributional semantic models—0
Domain Mismatch Doesn’t Always Prevent Cross-lingual Transfer Learning—0
Community Detection in Political Twitter Networks using Nonnegative Matrix Factorization Methods—0
A Framework for the Construction of Monolingual and Cross-lingual Word Similarity Datasets—0
Coming to Your Senses: on Controls and Evaluation Sets in Polysemy Research—0
A Quadratic 0-1 Programming Approach for Word Sense Disambiguation—0
A Comparison of Greedy and Optimal Assessment of Natural Language Student Input Using Word-to-Word Similarity Metrics—0
Comparing in context: Improving cosine similarity measures with a metric tensor—0
Comparison between the Structures of Word Co-occurrence and Word Similarity Networks for Ill-formed and Well-formed Texts in Taiwan Mandarin—0
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