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 WordNet and Semantic Similarity for Bilingual Terminology Mining from Comparable Corpora—0
A Clustering Framework for Unsupervised and Semi-supervised New Intent Discovery—0
Varanalysis@SV-Ident 2022: Variable Detection and Disambiguation Based on Semantic Similarity—0
Variational Inference for Logical Inference—0
Variational Information Pursuit with Large Language and Multimodal Models for Interpretable Predictions—0
VidHalluc: Evaluating Temporal Hallucinations in Multimodal Large Language Models for Video Understanding—0
Viewpoint-aware Video Summarization—0
VIFIDEL: Evaluating the Visual Fidelity of Image Descriptions—0
Vision Transformer Based Semantic Communications for Next Generation Wireless Networks—0
Visual Fashion-Product Search at SK Planet—0
Visualising WordNet Embeddings: some preliminary results—0
Visually Aligned Word Embeddings for Improving Zero-shot Learning—0
VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks—0
VRep at SemEval-2016 Task 1 and Task 2: A System for Interpretable Semantic Similarity—0
VUDG: A Dataset for Video Understanding Domain Generalization—0
Wasserstein distances for evaluating cross-lingual embeddings—0
Wasserstein-Fisher-Rao Document Distance—0
Weakly Supervised Multiclass Video Segmentation—0
Weakly-Supervised Video Moment Retrieval via Semantic Completion Network—0
Web-based Semantic Similarity for Emotion Recognition in Web Objects—0
Weight Initialization in Neural Language Models—0
What does this Emoji Mean? A Vector Space Skip-Gram Model for Twitter Emojis—0
What implementation and translation teach us: the case of semantic similarity measures in wordnets—0
What Makes Better Augmentation Strategies? Augment Difficult but Not too Different—0
What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning—0
Wild Devs' at SemEval-2017 Task 2: Using Neural Networks to Discover Word Similarity—0
Word Embedding Evaluation in Downstream Tasks and Semantic Analogies—0
Word Embeddings and Validity Indexes in Fuzzy Clustering—0
Word Embeddings as Metric Recovery in Semantic Spaces—0
Words Blending Boxes. Obfuscating Queries in Information Retrieval using Differential Privacy—0
Word Semantic Similarity for Morphologically Rich Languages—0
Word to Sentence Visual Semantic Similarity for Caption Generation: Lessons Learned—0
Word Vector Space Specialisation—0
YiSi - a Unified Semantic MT Quality Evaluation and Estimation Metric for Languages with Different Levels of Available Resources—0
Zero-shot Event Causality Identification with Question Answering—0
Zero-Shot Learning via Semantic Similarity Embedding—0
Zero-shot Learning with Minimum Instruction to Extract Social Determinants and Family History from Clinical Notes using GPT Model—0
Zero-Shot Text Matching for Automated Auditing using Sentence Transformers—0
A new approach for measuring semantic similarity of ontology concepts using dynamic programming—0
DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs—0
Enhancing Adversarial Text Attacks on BERT Models with Projected Gradient Descent—0
Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning—0
A Semi-supervised Multi-channel Graph Convolutional Network for Query Classification in E-commerce—0
TempRetriever: Fusion-based Temporal Dense Passage Retrieval for Time-Sensitive Questions—0
Towards Automated Situation Awareness: A RAG-Based Framework for Peacebuilding Reports—0
AI-enhanced semantic feature norms for 786 concepts—0
Evaluations at Work: Measuring the Capabilities of GenAI in Use—0
3D Compositional Zero-shot Learning with DeCompositional Consensus—0
A 2D Semantic-Aware Position Encoding for Vision Transformers—0
A bilingual approach to specialised adjectives through word embeddings in the karstology domain—0
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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