SOTAVerified

Semantic Similarity

The main objective Semantic Similarity is to measure the distance between the semantic meanings of a pair of words, phrases, sentences, or documents. For example, the word “car” is more similar to “bus” than it is to “cat”. The two main approaches to measuring Semantic Similarity are knowledge-based approaches and corpus-based, distributional methods.

Source: Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection

Papers

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Showing 1001–1050 of 1564 papers

TitleStatusHype
Using Text to Teach Image Retrieval—0
Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action RecognitionCode0
A partition-based similarity for classification distributions—0
Center-wise Local Image Mixture For Contrastive Representation Learning—0
Machine Translation Reference-less Evaluation using YiSi-2 with Bilingual Mappings of Massive Multilingual Language Model—0
From Disjoint Sets to Parallel Data to Train Seq2Seq Models for Sentiment Transfer—0
Comparative Evaluation of Label-Agnostic Selection Bias in Multilingual Hate Speech DatasetsCode0
Improving Knowledge-Aware Dialogue Response Generation by Using Human-Written Prototype Dialogues—0
How does BERT capture semantics? A closer look at polysemous wordsCode0
When is a bishop not like a rook? When it's like a rabbi! Multi-prototype BERT embeddings for estimating semantic relationshipsCode0
Surfacing Privacy Settings Using Semantic MatchingCode0
Deconstruct to Reconstruct a Configurable Evaluation Metric for Open-Domain Dialogue SystemsCode0
Second-Order NLP Adversarial Examples—0
Method of the coherence evaluation of Ukrainian text—0
Knowledge-Based Construction of Confusion Matrices for Multi-Label Classification Algorithms using Semantic Similarity Measures—0
On Learning Text Style Transfer with Direct RewardsCode0
GiBERT: Introducing Linguistic Knowledge into BERT through a Lightweight Gated Injection Method—0
Comparative analysis of word embeddings in assessing semantic similarity of complex sentences—0
Neural Passage Retrieval with Improved Negative Contrast—0
Rewriting Meaningful Sentences via Conditional BERT Sampling and an application on fooling text classifiers—0
Latte-Mix: Measuring Sentence Semantic Similarity with Latent Categorical Mixtures—0
CIMON: Towards High-quality Hash Codes—0
Meta-Context Transformers for Domain-Specific Response GenerationCode0
Webly Supervised Image Classification with Metadata: Automatic Noisy Label Correction via Visual-Semantic GraphCode0
FILM: A Fast, Interpretable, and Low-rank Metric Learning Approach for Sentence Matching—0
Does the Objective Matter? Comparing Training Objectives for Pronoun ResolutionCode0
Beyond [CLS] through Ranking by Generation—0
Second-Order NLP Adversarial ExamplesCode0
A Mixed Learning Objective for Neural Machine Translation—0
Improving Semantic Similarity Calculation of Japanese Text for MT Evaluation—0
Differentially Private Adversarial Robustness Through Randomized Perturbations—0
Semantic-based Distance Approaches in Multi-objective Genetic Programming—0
Time-Aware Evidence Ranking for Fact-Checking—0
SEEC: Semantic Vector Federation across Edge Computing Environments—0
MultiGBS: A multi-layer graph approach to biomedical summarization—0
TabSim: A Siamese Neural Network for Accurate Estimation of Table Similarity—0
FAT ALBERT: Finding Answers in Large Texts using Semantic Similarity Attention Layer based on BERTCode0
A Survey on Text Simplification—0
Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs—0
Measuring prominence of scientific work in online news as a proxy for impact—0
Logic Constrained Pointer Networks for Interpretable Textual SimilarityCode0
CORD19STS: COVID-19 Semantic Textual Similarity Dataset—0
Unsupervised Paraphrasing via Deep Reinforcement Learning—0
Unsupervised Semantic Hashing with Pairwise ReconstructionCode0
Text Classification with Negative Supervision—0
DeSpin: a prototype system for detecting spin in biomedical publications—0
Class-Similarity Based Label Smoothing for Confidence Calibration—0
Exploiting Non-Taxonomic Relations for Measuring Semantic Similarity and Relatedness in WordNet—0
Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network—0
MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measuresCode0
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All datasetsAnnotated corpus for semantic similarity of clinical trial outcomes (expanded corpus)Annotated corpus for semantic similarity of clinical trial outcomes (original corpus)SICKBIOSSESCHIP-STSClinicalSTSMedSTS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BioBERT (pre-trained on PubMed abstracts + PMC, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F193.38—Unverified
2SciBERT uncased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F191.51—Unverified
3SciBERT cased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F190.69—Unverified
4BERT-Base uncased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F189.16—Unverified
5BERT-Base cased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F189.12—Unverified
#ModelMetricClaimedVerifiedStatus
1BioBERT (pre-trained on PubMed abstracts + PMC, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F189.75—Unverified
2SciBERT cased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F189.3—Unverified
3SciBERT uncased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F189.3—Unverified
4BERT-Base uncased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F186.8—Unverified
5BERT-Base cased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F184.21—Unverified
#ModelMetricClaimedVerifiedStatus
1Doc2VecCMSE0.31—Unverified
2LSTM (Tai et al., 2015)MSE0.28—Unverified
3Bidirectional LSTM (Tai et al., 2015)MSE0.27—Unverified
4combine-skip (Kiros et al., 2015)MSE0.27—Unverified
5Dependency Tree-LSTM (Tai et al., 2015)MSE0.25—Unverified
#ModelMetricClaimedVerifiedStatus
1BioLinkBERT (large)Pearson Correlation0.94—Unverified
2BioLinkBERT (base)Pearson Correlation0.93—Unverified
3NCBI_BERT(base) (P+M)Pearson Correlation0.92—Unverified
#ModelMetricClaimedVerifiedStatus
1MacBERT-largeMacro F185.6—Unverified
#ModelMetricClaimedVerifiedStatus
1CharacterBERT (base, medical, ensemble)Pearson Correlation85.62—Unverified
#ModelMetricClaimedVerifiedStatus
1NCBI_BERT(base) (P+M)Pearson Correlation0.85—Unverified