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 751–800 of 1564 papers

TitleStatusHype
RELiC: Retrieving Evidence for Literary Claims—0
Representing Verbs with Visual Argument Vectors—0
Reserating the awesometastic: An automatic extension of the WordNet taxonomy for novel terms—0
Graph-Based Recommendation System Enhanced with Community Detection—0
ResSim at SemEval-2017 Task 1: Multilingual Word Representations for Semantic Textual Similarity—0
Rethinking Crowd Sourcing for Semantic Similarity—0
Rethinking the constraints of multimodal fusion: case study in Weakly-Supervised Audio-Visual Video Parsing—0
Retrieval-Enhanced Few-Shot Prompting for Speech Event Extraction—0
Retrofitting Concept Vector Representations of Medical Concepts to Improve Estimates of Semantic Similarity and Relatedness—0
Retrofitting Word Vectors of MeSH Terms to Improve Semantic Similarity Measures—0
Multi-view Semantic Matching of Question retrieval using Fine-grained Semantic Representations—0
Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task Embeddings for Coreference Resolution—0
Rewarding Semantic Similarity under Optimized Alignments for AMR-to-Text Generation—0
Rewriting Meaningful Sentences via Conditional BERT Sampling and an application on fooling text classifiers—0
RoMe: A Robust Metric for Evaluating Natural Language Generation—0
HTS-Attack: Heuristic Token Search for Jailbreaking Text-to-Image Models—0
RTM at SemEval-2016 Task 1: Predicting Semantic Similarity with Referential Translation Machines and Related Statistics—0
RTM at SemEval-2016 Task 1: Predicting Semantic Similarity with Referential Translation Machines and Related Statistics—0
RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity—0
RTM-DCU: Predicting Semantic Similarity with Referential Translation Machines—0
RTM-DCU: Referential Translation Machines for Semantic Similarity—0
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation—0
RUSSE: The First Workshop on Russian Semantic Similarity—0
SAIL: Sentiment Analysis using Semantic Similarity and Contrast Features—0
Saliency Attention and Semantic Similarity-Driven Adversarial Perturbation—0
Saliency-Aware Regularized Graph Neural Network—0
SAM-GAN: Self-Attention supporting Multi-stage Generative Adversarial Networks for text-to-image synthesis—0
Samsung Poland NLP Team at SemEval-2016 Task 1: Necessity for diversity; combining recursive autoencoders, WordNet and ensemble methods to measure semantic similarity.—0
SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression—0
Scalable Gaussian Processes for Supervised Hashing—0
Scene-Aware Label Graph Learning for Multi-Label Image Classification—0
Scene Recognition with Prototype-agnostic Scene Layout—0
Scientific and Technological Information Oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval—0
Searching for Legal Documents at Paragraph Level: Automating Label Generation and Use of an Extended Attention Mask for Boosting Neural Models of Semantic Similarity—0
Second-Order NLP Adversarial Examples—0
Securing from Unseen: Connected Pattern Kernels (CoPaK) for Zero-Day Intrusion Detection—0
SEEC: Semantic Vector Federation across Edge Computing Environments—0
SEED: Towards More Accurate Semantic Evaluation for Visual Brain Decoding—0
Seed Words Based Data Selection for Language Model Adaptation—0
Generating Is Believing: Membership Inference Attacks against Retrieval-Augmented Generation—0
SEF@UHH at SemEval-2017 Task 1: Unsupervised Knowledge-Free Semantic Textual Similarity via Paragraph Vector—0
Self-Controlled Dynamic Expansion Model for Continual Learning—0
Self-Critical Alternate Learning based Semantic Broadcast Communication—0
Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss—0
SeLIP: Similarity Enhanced Contrastive Language Image Pretraining for Multi-modal Head MRI—0
SemAligner: A Method and Tool for Aligning Chunks with Semantic Relation Types and Semantic Similarity Scores—0
Semantic Alignment with Calibrated Similarity for Multilingual Sentence Embedding—0
Semantic Answer Similarity for Evaluating Question Answering Models—0
Semantic-based Distance Approaches in Multi-objective Genetic Programming—0
Semantic Data Set Construction from Human Clustering and Spatial Arrangement—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