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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 31–40 of 1564 papers

Title	Date	Tasks	Status	Hype
SGC-Net: Stratified Granular Comparison Network for Open-Vocabulary HOI Detection	Mar 1, 2025	Human-Object Interaction DetectionLarge Language Model	CodeCode Available	1
Mitigating Hallucinations in Large Vision-Language Models by Adaptively Constraining Information Flow	Feb 28, 2025	HallucinationObject	CodeCode Available	1
MedFILIP: Medical Fine-grained Language-Image Pre-training	Jan 18, 2025	Contrastive LearningDiagnostic	CodeCode Available	1
DiffSim: Taming Diffusion Models for Evaluating Visual Similarity	Dec 19, 2024	Contrastive LearningDenoising	CodeCode Available	1
DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image Segmentation	Dec 17, 2024	Contrastive LearningImage Segmentation	CodeCode Available	1
Familiarity: Better Evaluation of Zero-Shot Named Entity Recognition by Quantifying Label Shifts in Synthetic Training Data	Dec 13, 2024	named-entity-recognitionNamed Entity Recognition	CodeCode Available	1
Vid-Morp: Video Moment Retrieval Pretraining from Unlabeled Videos in the Wild	Dec 1, 2024	Moment RetrievalRetrieval	CodeCode Available	1
RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data	Nov 27, 2024	Activity RecognitionContrastive Learning	CodeCode Available	1
Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning	Nov 7, 2024	Decision MakingFairness	CodeCode Available	1
CmdCaliper: A Semantic-Aware Command-Line Embedding Model and Dataset for Security Research	Nov 2, 2024	Line DetectionSemantic Similarity	CodeCode Available	1

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All datasets Annotated corpus for semantic similarity of clinical trial outcomes (expanded corpus)Annotated corpus for semantic similarity of clinical trial outcomes (original corpus)SICK BIOSSES CHIP-STS ClinicalSTS MedSTS

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	BioBERT (pre-trained on PubMed abstracts + PMC, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")	F1	93.38	—	Unverified
2	SciBERT uncased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")	F1	91.51	—	Unverified
3	SciBERT cased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")	F1	90.69	—	Unverified
4	BERT-Base uncased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")	F1	89.16	—	Unverified
5	BERT-Base cased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")	F1	89.12	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BioBERT (pre-trained on PubMed abstracts + PMC, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")	F1	89.75	—	Unverified
2	SciBERT cased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")	F1	89.3	—	Unverified
3	SciBERT uncased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")	F1	89.3	—	Unverified
4	BERT-Base uncased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")	F1	86.8	—	Unverified
5	BERT-Base cased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")	F1	84.21	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Doc2VecC	MSE	0.31	—	Unverified
2	LSTM (Tai et al., 2015)	MSE	0.28	—	Unverified
3	Bidirectional LSTM (Tai et al., 2015)	MSE	0.27	—	Unverified
4	combine-skip (Kiros et al., 2015)	MSE	0.27	—	Unverified
5	Dependency Tree-LSTM (Tai et al., 2015)	MSE	0.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BioLinkBERT (large)	Pearson Correlation	0.94	—	Unverified
2	BioLinkBERT (base)	Pearson Correlation	0.93	—	Unverified
3	NCBI_BERT(base) (P+M)	Pearson Correlation	0.92	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MacBERT-large	Macro F1	85.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CharacterBERT (base, medical, ensemble)	Pearson Correlation	85.62	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	NCBI_BERT(base) (P+M)	Pearson Correlation	0.85	—	Unverified