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 301–350 of 1564 papers

TitleStatusHype
More Than Meets The Eye: Semi-supervised Learning Under Non-IID DataCode0
Analyzing how BERT performs entity matchingCode0
Automatic Design of Semantic Similarity Ensembles Using Grammatical EvolutionCode0
KNN-Defense: Defense against 3D Adversarial Point Clouds using Nearest-Neighbor SearchCode0
Jmp8 at SemEval-2017 Task 2: A simple and general distributional approach to estimate word similarityCode0
Accidental Misalignment: Fine-Tuning Language Models Induces Unexpected VulnerabilityCode0
Investigating the Effects of Word Substitution Errors on Sentence EmbeddingsCode0
Joint Word Representation Learning using a Corpus and a Semantic LexiconCode0
Knowledgeable Storyteller: A Commonsense-Driven Generative Model for Visual StorytellingCode0
Autoencoding Pixies: Amortised Variational Inference with Graph Convolutions for Functional Distributional SemanticsCode0
A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence RepresentationsCode0
Ad Hoc Table Retrieval using Semantic SimilarityCode0
Auto-Encoding Dictionary Definitions into Consistent Word EmbeddingsCode0
Integrating Visual and Semantic Similarity Using Hierarchies for Image RetrievalCode0
Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change AnalysisCode0
Instance Smoothed Contrastive Learning for Unsupervised Sentence EmbeddingCode0
Table2Vec: Neural Word and Entity Embeddings for Table Population and RetrievalCode0
Augmenting Reddit Posts to Determine Wellness Dimensions impacting Mental HealthCode0
INO at Factify 2: Structure Coherence based Multi-Modal Fact VerificationCode0
Augmenting Neural Response Generation with Context-Aware Topical AttentionCode0
Improving Adversarial Robustness with Self-Paced Hard-Class Pair ReweightingCode0
Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text SummarizationCode0
Image Similarity using An Ensemble of Context-Sensitive ModelsCode0
Cross-Lingual Cross-Platform Rumor Verification Pivoting on Multimedia ContentCode0
Identifying Cognate Sets Across Dictionaries of Related LanguagesCode0
Effective and Imperceptible Adversarial Textual Attack via Multi-objectivizationCode0
Hypercube-RAG: Hypercube-Based Retrieval-Augmented Generation for In-domain Scientific Question-AnsweringCode0
HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on TextCode0
A Brief Study on the Effects of Training Generative Dialogue Models with a Semantic lossCode0
Counter-fitting Word Vectors to Linguistic ConstraintsCode0
HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive RegularizationCode0
Hyperpolyglot LLMs: Cross-Lingual Interpretability in Token EmbeddingsCode0
Harnessing Frozen Unimodal Encoders for Flexible Multimodal AlignmentCode0
Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse NetworksCode0
Hierarchy-based Image Embeddings for Semantic Image RetrievalCode0
Guiding and Diversifying LLM-Based Story Generation via Answer Set ProgrammingCode0
Historical Ink: Semantic Shift Detection for 19th Century SpanishCode0
How does BERT capture semantics? A closer look at polysemous wordsCode0
GSTran: Joint Geometric and Semantic Coherence for Point Cloud SegmentationCode0
HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community DetectionCode0
A mathematical theory of semantic development in deep neural networksCode0
Hybrid Semantic Recommender System for Chemical CompoundsCode0
Controlling Length in Abstractive Summarization Using a Convolutional Neural NetworkCode0
Global and Local Information Adjustment for Semantic Similarity EvaluationCode0
GenSense: A Generalized Sense Retrofitting ModelCode0
Identifying Semantic Divergences in Parallel Text without AnnotationsCode0
ImpliRet: Benchmarking the Implicit Fact Retrieval ChallengeCode0
Improved Semantic Representations From Tree-Structured Long Short-Term Memory NetworksCode0
Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual LearningCode0
GiBERT: Enhancing BERT with Linguistic Information using a Lightweight Gated Injection MethodCode0
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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