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 701–750 of 1564 papers

TitleStatusHype
Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task SamplingCode1
A large-scale computational study of content preservation measures for text style transfer and paraphrase generationCode1
Rewarding Semantic Similarity under Optimized Alignments for AMR-to-Text Generation—0
Binary Encoded Word Mover’s Distance—0
Improving Relevance Quality in Product Search using High-Precision Query-Product Semantic Similarity—0
Probing Cross-Lingual Lexical Knowledge from Multilingual Sentence Encoders—0
Word Embeddings and Validity Indexes in Fuzzy Clustering—0
SANA: Cross-Species Prediction of Gene Ontology GO Annotations via Topological Network AlignmentCode1
Suggesting Relevant Questions for a Query Using Statistical Natural Language Processing Technique—0
A Multi-level Alignment Training Scheme for Video-and-Language Grounding—0
Adversarial Contrastive Learning by Permuting Cluster Assignments—0
CgAT: Center-Guided Adversarial Training for Deep Hashing-Based RetrievalCode1
What If: Generating Code to Answer Simulation QuestionsCode0
Farmer-Bot: An Interactive Bot for Farmers—0
Towards Better Understanding of User Satisfaction in Open-Domain Conversational SearchCode1
Efficient comparison of sentence embeddings—0
Analyzing how BERT performs entity matchingCode0
Semantic Pose Verification for Outdoor Visual Localization with Self-supervised Contrastive Learning—0
A bilingual approach to specialised adjectives through word embeddings in the karstology domain—0
Cross-Media Scientific Research Achievements Retrieval Based on Deep Language Model—0
LinkBERT: Pretraining Language Models with Document LinksCode2
Attributable Visual Similarity LearningCode1
SMARAGD: Learning SMatch for Accurate and Rapid Approximate Graph DistanceCode0
Semantic Similarity Computing for Scientific Academic Conferences fused with domain features—0
From Stance to Concern: Adaptation of Propositional Analysis to New Tasks and DomainsCode0
RELIC: Retrieving Evidence for Literary ClaimsCode1
RoMe: A Robust Metric for Evaluating Natural Language GenerationCode1
Scientific and Technological Information Oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval—0
SimMatch: Semi-supervised Learning with Similarity MatchingCode0
MarkBERT: Marking Word Boundaries Improves Chinese BERTCode1
Frequency-driven Imperceptible Adversarial Attack on Semantic SimilarityCode1
RACE: Retrieval-Augmented Commit Message GenerationCode1
Just Rank: Rethinking Evaluation with Word and Sentence SimilaritiesCode1
'Tis but Thy Name: Semantic Question Answering Evaluation with 11M Names for 1M Entities—0
Supervised Machine Learning Algorithm for Detecting Consistency between Reported Findings and the Conclusions of Mammography Reports—0
Attention-based Cross-Layer Domain Alignment for Unsupervised Domain Adaptation—0
SemSup: Semantic Supervision for Simple and Scalable Zero-shot GeneralizationCode0
JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning—0
Autoencoding Low-Resolution MRI for Semantically Smooth Interpolation of Anisotropic MRI—0
Moving Other Way: Exploring Word Mover Distance Extensions—0
Semantic Similarity Computing Model Based on Multi Model Fine-Grained Nonlinear Fusion—0
Language Models Explain Word Reading Times Better Than Empirical Predictability—0
Duplicate Detection in a Knowledge Base with PIKA—0
AI-based Approach for Safety Signals Detection from Social Networks: Application to the Levothyrox Scandal in 2017 on Doctissimo Forum—0
Efficient Embedding of Semantic Similarity in Control Policies via Entangled Bisimulation—0
Learning Deep Semantic Model for Code Search using CodeSearchNet CorpusCode1
Discriminative Supervised Subspace Learning for Cross-modal Retrieval—0
Reading-strategy Inspired Visual Representation Learning for Text-to-Video RetrievalCode1
Reinforcement Learning with Large Action Spaces for Neural Machine Translation—0
Task Formulation Matters When Learning Continuously: A Case Study in Visual Question Answering—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