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 71–80 of 1564 papers

Title	Date	Tasks	Status	Hype
20min-XD: A Comparable Corpus of Swiss News Articles	Apr 30, 2025	ArticlesSemantic Similarity	CodeCode Available	0
Homa at SemEval-2025 Task 5: Aligning Librarian Records with OntoAligner for Subject Tagging	Apr 30, 2025	RAGRetrieval	—Unverified	0
Retrieval-Enhanced Few-Shot Prompting for Speech Event Extraction	Apr 30, 2025	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
ReCellTy: Domain-specific knowledge graph retrieval-augmented LLMs workflow for single-cell annotation	Apr 24, 2025	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
Cyc3D: Fine-grained Controllable 3D Generation via Cycle Consistency Regularization	Apr 21, 2025	3D GenerationSemantic Similarity	—Unverified	0
Stay Hungry, Stay Foolish: On the Extended Reading Articles Generation with LLMs	Apr 21, 2025	ArticlesSemantic Similarity	—Unverified	0
Exploring Language Patterns of Prompts in Text-to-Image Generation and Their Impact on Visual Diversity	Apr 19, 2025	DiversityImage Generation	—Unverified	0
Towards a Multi-Agent Vision-Language System for Zero-Shot Novel Hazardous Object Detection for Autonomous Driving Safety	Apr 18, 2025	Anomaly DetectionAutonomous Driving	CodeCode Available	0
CDF-RAG: Causal Dynamic Feedback for Adaptive Retrieval-Augmented Generation	Apr 17, 2025	RAGRetrieval	CodeCode Available	1
Semantic Similarity-Informed Bayesian Borrowing for Quantitative Signal Detection of Adverse Events	Apr 16, 2025	Bayesian InferenceMarketing	—Unverified	0

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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