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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 351–400 of 1564 papers

TitleStatusHype
Persona Extraction Through Semantic Similarity for Emotional Support Conversation Generation—0
SAM-PD: How Far Can SAM Take Us in Tracking and Segmenting Anything in Videos by Prompt DenoisingCode0
GPTSee: Enhancing Moment Retrieval and Highlight Detection via Description-Based Similarity Features—0
EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic SegmentationCode2
API Is Enough: Conformal Prediction for Large Language Models Without Logit-Access—0
Semantic Text Transmission via Prediction with Small Language Models: Cost-Similarity Trade-off—0
PaECTER: Patent-level Representation Learning using Citation-informed Transformers—0
Investigating Continual Pretraining in Large Language Models: Insights and Implications—0
BiVRec: Bidirectional View-based Multimodal Sequential Recommendation—0
Sequential Visual and Semantic Consistency for Semi-supervised Text Recognition—0
Efficient data selection employing Semantic Similarity-based Graph Structures for model training—0
The Impact of Word Splitting on the Semantic Content of Contextualized Word RepresentationsCode0
Semantic Mirror Jailbreak: Genetic Algorithm Based Jailbreak Prompts Against Open-source LLMs—0
On Defining Smart Cities using Transformer Neural Networks—0
UMBCLU at SemEval-2024 Task 1A and 1C: Semantic Textual Relatedness with and without machine translationCode0
Semantic Textual Similarity Assessment in Chest X-ray Reports Using a Domain-Specific Cosine-Based MetricCode0
Reasoning before Comparison: LLM-Enhanced Semantic Similarity Metrics for Domain Specialized Text Analysis—0
SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 LanguagesCode1
OrderBkd: Textual backdoor attack through repositioningCode0
Large Language Model Augmented Exercise Retrieval for Personalized Language Learning—0
Multi-Lingual Malaysian Embedding: Leveraging Large Language Models for Semantic Representations—0
Reconstruct Your Previous Conversations! Comprehensively Investigating Privacy Leakage Risks in Conversations with GPT ModelsCode1
HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on TextCode0
In-Context Learning for Few-Shot Nested Named Entity Recognition—0
Benchmarking Transferable Adversarial AttacksCode1
Enhancing End-to-End Multi-Task Dialogue Systems: A Study on Intrinsic Motivation Reinforcement Learning Algorithms for Improved Training and Adaptability—0
Autoencoder-Based Domain Learning for Semantic Communication with Conceptual Spaces—0
It's About Time: Incorporating Temporality in Retrieval Augmented Language Models—0
Investigating Large Language Models for Financial Causality Detection in Multilingual Setup—0
Connecting the Dots: Leveraging Spatio-Temporal Graph Neural Networks for Accurate Bangla Sign Language Recognition—0
Using LLMs to discover emerging coded antisemitic hate-speech in extremist social media—0
PhotoBot: Reference-Guided Interactive Photography via Natural Language—0
Noise Contrastive Estimation-based Matching Framework for Low-Resource Security Attack Pattern RecognitionCode1
A character-based steganography using masked language modelingCode0
Image Similarity using An Ensemble of Context-Sensitive ModelsCode0
MaskClustering: View Consensus based Mask Graph Clustering for Open-Vocabulary 3D Instance Segmentation—0
HiHPQ: Hierarchical Hyperbolic Product Quantization for Unsupervised Image RetrievalCode1
Do Vision and Language Encoders Represent the World Similarly?Code1
Estimating Text Similarity based on Semantic Concept Embeddings—0
Recognizing Similar Crises through the Application of Ontology-based Knowledge Mining—0
Semantic Similarity Matching for Patent Documents Using Ensemble BERT-related Model and Novel Text Processing Method—0
PeFoMed: Parameter Efficient Fine-tuning of Multimodal Large Language Models for Medical ImagingCode2
Saliency-Aware Regularized Graph Neural Network—0
Are we describing the same sound? An analysis of word embedding spaces of expressive piano performanceCode0
AHAM: Adapt, Help, Ask, Model -- Harvesting LLMs for literature mining—0
MMGPL: Multimodal Medical Data Analysis with Graph Prompt Learning—0
An Empirical study of Unsupervised Neural Machine Translation: analyzing NMT output, model's behavior and sentences' contribution—0
Def2Vec: Extensible Word Embeddings from Dictionary DefinitionsCode0
Binary Code Summarization: Benchmarking ChatGPT/GPT-4 and Other Large Language ModelsCode1
Explicitly Integrating Judgment Prediction with Legal Document Retrieval: A Law-Guided Generative ApproachCode1
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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